{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76985004799e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print 'Testing affine_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  6.0217367793e-09\n",
      "dw error:  5.5901870384e-11\n",
      "db error:  6.23923779477e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print 'Testing affine_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 4) (3, 4) (3, 4)\n",
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "print x.shape, out.shape, correct_out.shape\n",
    "# Compare your output with ours. The error should be around 1e-8\n",
    "print 'Testing relu_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 10) (10, 10)\n",
      "Testing relu_backward function:\n",
      "dx error:  3.27560874418e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-12\n",
    "print 'Testing relu_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  4.82948978953e-10\n",
      "dw error:  2.35429706565e-10\n",
      "db error:  2.54800563219e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print 'Testing affine_relu_forward:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.99799241411\n",
      "dx error:  3.0387355051e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.30238479505\n",
      "dx error:  9.47016462042e-09\n"
     ]
    }
   ],
   "source": [
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print 'Testing svm_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print '\\nTesting softmax_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.34e-10\n",
      "b1 relative error: 4.73e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 1.37e-07\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-2\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print 'Testing initialization ... '\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print 'Testing test-time forward pass ... '\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print 'Testing training loss (no regularization)'\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print 'Running numeric gradient check with reg = ', reg\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.302829\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.175000\n",
      "(Iteration 101 / 4900) loss: 1.900713\n",
      "(Iteration 201 / 4900) loss: 1.669028\n",
      "(Iteration 301 / 4900) loss: 1.583955\n",
      "(Iteration 401 / 4900) loss: 1.468363\n",
      "(Epoch 1 / 10) train acc: 0.450000; val_acc: 0.453000\n",
      "(Iteration 501 / 4900) loss: 1.524762\n",
      "(Iteration 601 / 4900) loss: 1.559421\n",
      "(Iteration 701 / 4900) loss: 1.614787\n",
      "(Iteration 801 / 4900) loss: 1.422135\n",
      "(Iteration 901 / 4900) loss: 1.407487\n",
      "(Epoch 2 / 10) train acc: 0.500000; val_acc: 0.470000\n",
      "(Iteration 1001 / 4900) loss: 1.502186\n",
      "(Iteration 1101 / 4900) loss: 1.623756\n",
      "(Iteration 1201 / 4900) loss: 1.302797\n",
      "(Iteration 1301 / 4900) loss: 1.557025\n",
      "(Iteration 1401 / 4900) loss: 1.734979\n",
      "(Epoch 3 / 10) train acc: 0.528000; val_acc: 0.493000\n",
      "(Iteration 1501 / 4900) loss: 1.427331\n",
      "(Iteration 1601 / 4900) loss: 1.253327\n",
      "(Iteration 1701 / 4900) loss: 1.281298\n",
      "(Iteration 1801 / 4900) loss: 1.177972\n",
      "(Iteration 1901 / 4900) loss: 1.415444\n",
      "(Epoch 4 / 10) train acc: 0.528000; val_acc: 0.495000\n",
      "(Iteration 2001 / 4900) loss: 1.180424\n",
      "(Iteration 2101 / 4900) loss: 1.514437\n",
      "(Iteration 2201 / 4900) loss: 1.260166\n",
      "(Iteration 2301 / 4900) loss: 1.362753\n",
      "(Iteration 2401 / 4900) loss: 1.269705\n",
      "(Epoch 5 / 10) train acc: 0.531000; val_acc: 0.489000\n",
      "(Iteration 2501 / 4900) loss: 1.351374\n",
      "(Iteration 2601 / 4900) loss: 0.974082\n",
      "(Iteration 2701 / 4900) loss: 1.237603\n",
      "(Iteration 2801 / 4900) loss: 1.162243\n",
      "(Iteration 2901 / 4900) loss: 1.134247\n",
      "(Epoch 6 / 10) train acc: 0.582000; val_acc: 0.523000\n",
      "(Iteration 3001 / 4900) loss: 1.386280\n",
      "(Iteration 3101 / 4900) loss: 1.232035\n",
      "(Iteration 3201 / 4900) loss: 1.386039\n",
      "(Iteration 3301 / 4900) loss: 1.111507\n",
      "(Iteration 3401 / 4900) loss: 1.231163\n",
      "(Epoch 7 / 10) train acc: 0.582000; val_acc: 0.477000\n",
      "(Iteration 3501 / 4900) loss: 1.140196\n",
      "(Iteration 3601 / 4900) loss: 1.235712\n",
      "(Iteration 3701 / 4900) loss: 1.060803\n",
      "(Iteration 3801 / 4900) loss: 1.352212\n",
      "(Iteration 3901 / 4900) loss: 1.291343\n",
      "(Epoch 8 / 10) train acc: 0.580000; val_acc: 0.520000\n",
      "(Iteration 4001 / 4900) loss: 1.244054\n",
      "(Iteration 4101 / 4900) loss: 1.122127\n",
      "(Iteration 4201 / 4900) loss: 1.136476\n",
      "(Iteration 4301 / 4900) loss: 1.245353\n",
      "(Iteration 4401 / 4900) loss: 1.287869\n",
      "(Epoch 9 / 10) train acc: 0.599000; val_acc: 0.513000\n",
      "(Iteration 4501 / 4900) loss: 1.176297\n",
      "(Iteration 4601 / 4900) loss: 1.194772\n",
      "(Iteration 4701 / 4900) loss: 1.417329\n",
      "(Iteration 4801 / 4900) loss: 1.175872\n",
      "(Epoch 10 / 10) train acc: 0.585000; val_acc: 0.507000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "pass\n",
    "solver = Solver(model, data,\n",
    "                    update_rule='sgd',\n",
    "                    optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                    },\n",
    "                    lr_decay=0.95,\n",
    "                    num_epochs=10, batch_size=100,\n",
    "                    print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RGBMFyoEDQF7CqzEgjLV72LVrV5eNvmOxGJWVS6ivX8ShQ9dx6NB11NcvorJyScaag6vh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0sd\n6O4aCzcjaNwRAh03WYgeh6mqTyYrIpBFTiDwuJOaer2Tnj7MCQanqT1QcqBmZv7KWbhwmTNp0hNO\nOCyEKcHg44q8R7NHJvaxOyfpVMcQumhCn01OfYKi3o53nU1kqu3qeNkm6++PmZm/crFA6/lzs7qv\nm6tf9bgn9s00NW6JeWXjTjg8UM1bPX/cxFSJef1M7rtA4HGP7vLBB9tcyeLd7Xf/7ZZhb/s++GCb\nYtVc7gQCjzvhcG9n8eLnPbpSmzYDnNTUAQnsyZpsTbOLunPBufd6TVak2UfdbNYN56NctGiRIiDR\n+oubXG6yI8Q6vRxJTu/Nr+tmljbrc50at8T9uWGiOrje8fuXJMhE4r75j+ur/F+ToliWlYOcOF3o\nOE7HBr6/CCh1HOewZVnXAg86jnPRMZ7lnOh9p8r/r2JIUS4DdL61EQk56749wpevSyRj2zbZ2aPY\nujUPQxrgEAotYcOGh8jO7nDCd8ViMcrKygB4+ukt9YhMMjNHAiR8vp2UlAlYVn/AIhyWPHPl5dPr\n3Ttr1iB8Pt/R9pg66wPXIxBvmT4wnYtlRenY8d2vRciS2C6o34fFxTvo02cS5eXdMYfTuyEW0dMR\nC/gHwLVYlkPHjpsarINpgybr8B62dxPjaAtwbu5jRKODEet9J8RCm45YUncgnhoHsaqvQyzuPRFL\nbzlCgjAVc4Bd5/kx/Z2e3o/9+3tx5EgLzMHrSUgIhz6o/jRiRZyWcO9gHAcqKg4h1um9iDwBFJCc\n3IIzz9zCgQNLMUQDe5CQnI6q3t6D3snJz9Cy5Q4V6iqkQrNn5zFs2Cw1/u6wn7FICFhDh8aF9Mfn\n+xm2vQW4mWAwQLt2hQ2S/xjyoW6IlXkHch5oDGIt/yMmPGwRYgXtj5e85QXVv30QAoyZPPhgV+6/\nf9RROXbLWXFxsXrnEcTb1F6Nkx8Z77bUJ1JJzNmkD/xrwqI8hEjhLiQ8eB4iIz51jT68r8lu9Fzy\nYbwa/XSvYMgLZiDeuA8Q70oZIv8/ROTSSxgUDPaitjYZOAuxIntJcSRsEoycgQkZm6L6uA+GNOBF\n9b5c1e4hGMIVTcDgQxNtWNYVhELJnHPOKr744u8cOtRK1b8AOT92IxLydgGGpEPqYFmDcZy5qm/+\novrjPeRs3rNqPFoi3oa7Ec/IHsQ6/iHiCXI/E0KhVWzY0IpduyoZMEB7nJpjyDs0Kcd8xJYMkEla\n2vVUVV2BzPvmeOU8inh47kTClucj4XYvIvPxS8RDu1ldr/vIvXachYR/bVfX3KD65WzVZk0kMQrx\n1tuqbe5noN6XgsiwJjDZi8jcLchc1+QltyNjZiFemlupTwYlZFuxWDaxWLKqkw+RhwJ1XRDxeOSq\nNmYh4c51iFf7YUTOtezJ2XFp02pEhssxBBz6/OsIDGGGJm7pgmW9ruSi/lpYf88rQmSiEEPuoQlN\nbkDmUSmyFmqCIL2OabKQn2LWe1RfVqmfzqqt96i6ViJyqo9YHEIiNLykPIHAKCCHaFSvNXuQvWwN\nEvprYdabImQMu7vGR+8lIWAfltWNYDBZkaGNIBaLkZMzQRFWaVKmICKrjalP/mXWjPT0ASxe/GsA\nunSp4siRHhiCl87AJpKTy0lKug3H+QTbfplYrAmOc8PRvGxLl95Fv37PUl6uyY7Es+z3v0Jh4RQq\nKnawaVMRb731N6qq0qmtbY5er8UzGUQIXjYislGNyNEjGJIjTfDUHVmH5mHI4sxxivT0p9ixYzE+\nn4+ioiIGDJih9J1fqD4EiRBZhsztZxKeI2P205+O4r33phAO96aqaiCGgKwVsu+PU/UMI3vVacha\n2UU9Yx2tWn3Bnj1Vqt/d5EBrsKzVBIM98PmSjuqr3zQP3bdNinJSLJeWZTUH1jYE6BKu+yGw3XGc\n1GN8fwrQnSr1SqLiVlJSekzmy39F0aDTzXi0bNm444K5xHtNYtQXgBD79/8cEAB7zz2dyc/3u5g5\nbeov4AIIUlJOT0g2Xn8xsW2bJUtWMW7cWg4e7IrjfIJlFWLbt2JZDmlpzzFhQk/69u2p6njyAPd4\nrKS6xGIx2rcfoRbj0Yjiqxdls4CnpT1KZeUq/H5vyINt2yxdupS8vCCRSCuEjMLLfpXIdLp48WIG\nDgwgG0gMOScTRhSbp1V/foUs3Jcjm/xbCPAoQpTDezCgPQ48RdOmLfjqq14IqHaPlZspbgtGQdCK\n21+RjUI2zzZt3gYsxZymN6KnMcqon0BgFNFonus5mtFsDxLC9C6Jm3pycg/q6p7XowPYdOy4gDFj\nOvPww2+wf/+1xGLVRKNvAT9S7ddJ4b2gITn5aeAd9TzvBllUNNUjG0VFRVx66UZqaz/AMP79FAnZ\nug/DGqmTH6dhWB33IeeOeiBnfMy7AoFb+Pvf17B9e1k9ORszJkf1fRQBpmfgTTSrGQxRn7sB2PNI\nmFkKouy0RpSvd5BD9UMQJVErwK0QReA3iELeDy+TXww5j/N9TMJ2rZSud41TCaJA/BGjpOr+uhE5\ntzaHzz+/g2g0A/ilGpv5njGV9t2FkQFtnNGsqK0RpaoCw856i7r/dXVNX+qDk0oEWGt5+xuisHbH\nKOq6HlECgceIRnUCYxmzcHgwNTWH2b//AkS5XoIohusxAGek+j0AOUt3GjIf/46Aas1cWASUEQx+\nzDvvdKGiooLbb/+YaNSHgMK2mFDVliTKcCBwF9GoPm+lQ7X0uhpDxvkaZCy14eUZZIyzVT+2wwsm\nYqSmvsKkSdfzwAN/orr6R+qeYgTk1CGGhN9g1o9q1e8+BDC6jRsHEeNHImMjwC6aNl3MgQPDMMyn\nryPnNzch7LbucQSRoyqSkzcAFxKLleA49yPh5n9DAHQrNS56rRiJyMDHiGy/ocbBzQA9Ur13ARJG\nOgQBRbrcgMyHFqr/3PuWBlsxDIumKaHQk0BLIhFNGlKEhIyejwHtRUApPt8mzjjjUw4dmoAAzD4Y\nUOFmZuyJjLlbAdfrwnIEfPRCxn4BQppVgcw1zfqq66OBaStEzjapNhbgDXVtiWFc1Mai+1U/uveI\nhoGLMRZPR4whq1110ey5iczHhpUzFLJIS1tDVVVPxVbprvtVBIMWzZs/z/jx3bn11u4UFxdTWloK\nQEZGBrZtc9FFuxFjhDZad1H1/yOydluqj9wAWgzDItuaHTwVmfMbEKODNsRuVp8XYNZSXcdrAIdw\n+HVWrBgNcHTdj8f34/MVEIvlEot9iONcjYC5LoixKdFgIyUYnEaTJuuoquqDrIngNWBNRc7UanZg\nzWqtYcs2Row4g+nTkxFDoZ5vM9S7o6SlLefRR/vRt2/Pf8jZ8F0HdGOAdMdx8o/x/SlAd6oct3i9\nS8dXKv+v6/V1PYbHakuiV814HvTCdOxUAQUFaZ5D5on1EA/ZE1RUgONcD1SSlvYBq1c/iM8X89xX\nXLyjHl3v8axNX2dsNPArLW1Nbe3r1Ld+Npx+Qt+3a1cqkUhzjNfr+O/0Ajq5Ru7LQwDGRZjNG2Rz\nqgAW4ffnEIuFEOVJW8qLCAQuYsOGHE9/A64+0Ep1DmIN1oxkiYqwTTg8herqbCKRNhiPoaa6jxII\nrCAa7Y5Yct2evtHIJvh76gPOx0hOvpe6ug7IhpSGkIyUEQj0JynJxznnrOSLL77g0KGrMUQaDdFQ\nxwgGr6a29lfUB8+rKSxs6RmnWCzGaaf1IhI5EwFJ01RbF6j+0G3IU+/RHjLU59mIl6Qf3rKEefPq\nmDbtw3py1rHjXRw5UsPu3SlqjAZgFEXdny0Q5cFBrNxXq2tzEfCyGGF0bI0o4SCKwZOIQq4/14qa\n29u62DU+k9TzEsfkWYxV18Z4PLzpE2Auw4f7efvtGqqr04lEWqjrStQzm6rrRVkXRbyVqqO7P7WS\n1RxRAm9Bzj9qAO32UGpvSb7qq70I8EtHLNVnqe8aSt2i+7clllUMXEswmExq6losK8TevUHq6s5B\nZGyuusdNQ75S9c+5iMJ7JgLm3J7Sya427yMQ2Ihl9aO2di1iiDis7tEeo4aAwgpse5ny1C/ApHd4\nSfVXb8SDfzui0GmlU6eYaIYxEgigaNPmJUpLl1JSUqLWaa3Qau/6bARI9MOAMxuYjc+Xjm1/hEl7\nsQqRoVkIeNCebNQ9XZk7ty8jRqwiGh2LyGIaoiDfhfHugvG0utcc7VGcDlxIcvJebHsD8fjpiGxo\no8pGxMBxCcb76fb8CUmOKPr9EdnaiJnP5wAf4/NtwO+/n2hUznMa0KS9VavxppaQ/jEpEDSAiiFz\n9VcYj4r2JG5EQIM2qnyEAV4mhYrfv594vADH0X3qNl6ci8xtneJlAeId1ukL3OBQn1fWaUCuRtbN\nTer3C6o92kCkU2A0Q9Y0nU6mZUJdpeh9D1Dy5EdA5x2u+um55/aE9Uqop1m7U1J6q7O8NPC92S8T\njeVnnTVdebTbUD+NAsiZVzfId8ugGyDVqnsKkDX4dUTO85FzjRGE3MQNoHJU/xSycOEg+vfvlRBZ\nVIykO3qKsWOvZ9++fTz6aEgZd9xpddz7lY4Y0CCzIYOCjd9/G7FYO0zUizGqBIMVjB1bwfjxYUT2\nju0J/Ed10u9s2gLLsi5HTDj3Hu+6Bx988OhPQUHBt/X6U+U/pJyI+fJfVb5JiohjtaWiosvR52lQ\nV5+Zs2HDx/HqYds2Q4ZMp7z8dOQgcB9gLFVVa8jLm01WVtbR+2zbpk+fafXoevv0mdYg2Yv2mu3a\n5WYEk/Y0zJQpZDYbN+awYMGtCWyUDRc3k2ck8msMG6Mmb1iNZS0lM/Oueiybffr0IRBwU977MGAO\nBOjciPeQcwA4g9tv/4T09B2I8t4K8eCNpkWL9+rREXsJY4Q4RwhJLqVNm8GI5+kaDINlNtCJ6ups\nbNs9ppoIpQV+/7v4fP2RzeUlDK24H6NA/VX97owoOYuBntTVORhLfUvgAPA80WhPamocdu+OcujQ\nlRiiCx9iddTsmCsIhVYSDvfHca7FS4hhxiWxlJSUEI9fqN7pJpC4FLGG63QIszDEM/mIkpRKUtLj\n1D+MbgNVvPTSSyoE2y1npWzf/hV79thIKNttCfdKf4ZCMebNu5lw+ExEKStWfViIeHXGIQp9JoZ+\n3I9RuLMQALUKQx4yAvFaLUcsxCMRr96NGNa6FqovUjH020UI016Sq566/3N55ZUvqKiYSSQyBkOe\nlIlhupyPgMdtql5/Q2TgJxgacTez5xL1jnswaQI0Xfq9SGjjhciY3IbImI2ER12EsAomkl2AlyV0\nJI5zJ44T45xzVgLJlJdPp65uEKII6367FK8stVZ1fEz19ecIUNAyfh+GYr4nUE00+hy1tb3Vd4cR\n5U2HZq5XbfTWs23bDcyePQjLmqPqciMC/OcgstcH8TrtRpRKTR7TAVE8P0VTqVtWBenp61m5csLR\nKAKZC2buQhp+f2uSk7Ws+hBFdhHQHNs+R/V/awxD6XWqrzJVW1eon2uBFIYP/0wR6jyJAN4k9dw7\n8BL9dEbWswGYNQdVv2kEg/sZO9Zm/PjL+cEPyjByaCMhjOMRsLja9Q5NXb8A8d51UWOUrL7TYO4T\n4M+cc057olE9J/6C2bey1HN7YAg/DKFGNHoRMpf0+JWo9hWocclDwG6F+lyvFdmYuWIjQKEvqamz\nmTXrTObO7Uta2iCCweVY1hOIPC1AQlx1ipcsZPyzMAQ37ra7Wa1LEK/hB8ia+y4CSv4bAQyHEUPE\nxYh8NkbWlyVIKHTiPm5j25WUlpZi2za2HVN1fFK1UbdLM776ECBbiAEdXUhkq7Tty0hPz3cRYoGA\n38VAEWVlnSkqKqqXJqqqahJiNNjjem4RAk4TWTFLEZB+MQYwz0LkuQl+/4PI+O1B1pJ8ZL29Ud3T\nCwl5nIrMiVtVX69l0qQ3WLx4sdIv3Hv0JnbvPkJ+fojHH08hGm2GrM/rMGueew3QzLjZyLrYEm/6\nGgAffn8HgkEdUq77WdKWpKW9wVVXXUUgsFy1wZ1+ZwtJQwAAIABJREFUyjzjm+ikBQUFHgz0bZdv\nBdBZltURmeldHcf54njXuhvTpUuXb+P1p8qp8h0uJ/ZIJ7JKpqTsIRRKXKhOnH+vuLiYsrI06i/E\nPsrKOnsWn6KiIhcTprmuoqILRUVF5q0qfDMjI5+8vE+IRE7ew67B54ABAxpgo6zfHi8A1hvsKODP\nBIOXEA6vYtEiPx9++Lt6XkSfz0eTJikY9quVwAwaN44SDA5CFLU6Eqn1YSFvv13D0qUj+elPRxMM\nbsCynsWy9rBnz4WcdlpPLrmkktzcj8jOHkVx8Q4P8+r69a3YtWsZ998/ku9973TkLEp91izLaknz\n5uV4afhFIW/Z8qACp9oDppn03BtmGNhDUlI5llWNWIlvRFgLwSQc7oZsiCOR8KixiPV1X8K7Jd9f\nOPwchYUtWbRoNJbVgoaU+bS01xuUO8v6C16wonNvXYFYNHUC1lLVjmtVPQr47W/7YtIVgMm/1py1\naztRU5PIQjgDx5mLbR/BeMkSFXpo124/7du35+OPb1D9+3MMVXV3RIkYpvrpZ4jRYB2iIC9XTxmB\nhMVF1P8ZCNjQ1OHTELl0K4Naie+KZel8XGWIYpSYr1DaU12t61iKUdSnYMI8fYiRwQF+h9hL9yOK\ns7vUIHKn6eefR8DD5RhFJAOTi06D5WTgD4jC+glepX4+ogy6lUitaFUDX1JZeZCKip9jgOVBBHTG\nEHktcLVbK/jnI0aHzhjAdwTxFOn1KJFKvj0i69eq/zUlu073ISydlnUb99yTy7nnNiclZbDq/43q\nObo/tyOA4FUkDNxdR2FUbNKkirlzjzB/fozFi/PJzMwAIDMzE9vW67Ie84XEYhcRi2kFXIPfPNVf\ny1yflyJh3noNPR9Zp4R9V0LZllFXtwc5K7YECSt250mcj4TlriA5Wb+roRx+pdTWvsfEiZVMmJDM\nV1+djlcOr1NtWI7Ig56L7THMpR+quroZSUFk61Is6/tUV88HfoxJ7aFTYfgQj2AMb34zTWnfn0hk\nIikpPWnUaDWh0FtYVhwZ94sQdVLn7dOGLv3cPASYD0KAzh+orv45Q4e+zdChFp9+2pUmTeaqtCF6\nLxEjlmUNJhhciaSmqKH+mjgArzGiUo3B9RjjwyJkPchX43En4gUaiHjTRmOYaTeoPihCDAwjqa1N\nZfjwFPLyFtKo0dMYxscXVD9VI2eme2JZSwgEpuH3lyLrg2aN9ZakpGYsXjycWbPOJjn5E3XtU6rO\nu4lEnuO1195uwMCchcjfKxhGaZ2jUn9foN45HUlXEcRr5OkJPEUs9keaNYOkJG2EiKi2Wep5n+AF\njrqUUlFRS17eX4hE3Cy5rdW7X6Suro8yfG1GDDJliEFCr3mrgCfw+0eQnKz3pAgC5v+Id/2NkZpa\nRFqaDjPWa8hK/P4cIpFaLr/8HWIxC4km6EDiPvNNS5cuXf6pgK4hvs6GisUxOM8ty0pDemOg4ziV\n31bFTpX/n0W8VQvYurUb3vNj75CVdcu/smrfoARwnCV4KfsbbosGCQZ0TWTYsFEespi5c0d8ayGn\nZWVlNJTHznGSKCsro1OnTkdDMrdtO6w8fiCLX88TtsddNGAdOtTbntmz8z1hrPWL9rpMYcaMpmRk\njAFgy5YtVFRU0LZt26Mex+LiYg4e1DTH+9T9FocOpSqr+jgknOcuEkHsxx9fC0TZsuVpdfZvPrCD\nWGwVssn6iEZh69ZbGDrUhFm4QxANQM5GNnyv/LZtu57Zs0fRr99kKioGJxxMH6dITG5BLJb/jSi/\n6zGhLT2BGGeffR2ffZZPJAKSI68WL4W53hB1qEw2EgYI4uW7TV0fIxxex4oV4wEYMmQ60ehhxLsz\nCjf5z9KlD9WTu6ysLJo3f5KKis8RS35XBDhNQDbZtxBLdmvMBq2fcTczZ15HUpJDPD4S8eStRRPQ\nRKP67ItObqvTfTyn3vMaMqe0Qp8L1JKZuYm5c+/AtiMYYpP9GAVChzv+jaSkC3jggb9jWZcwe3YR\n+/cPw3HS1XP7IF6IzUiojgY02xGlTSuIf8Ak/NbJ6H04TgdV/8YIUYCl/u+irn0bv98mFgtgFKP5\nGBKKR9XYRRFAoINeNPU/qt29Vd06Ip5l7S2UvFM+3y58vjixGOqzXgghQSriHXpE9aFWOF9C5KxC\njelliGKYjhgF9Djq909R9QTxZNyg2luCgLZ83LLk9+8jFuuq3nUBoqx1Q5TUZPUcrVC6w81tBKRe\niCjJ98PR3Fu6vRAKdaVdu1aAsHebJM3XuZ7zCAIeHQQYayD9U/Wc6zh0qBu//OVaHKcfPl814fBC\n7rmnM7FYDdHopapNmmRjvnpfULW3pXr+TIQ8pIfqlyYIKNih3nsromx2Q0BEEfA9TMqTUgTYD1b9\ncxfiTQPYR7NmYVJSnqeiojf115yYanczHOcZ9fw7VX11yJz2spyr2v4aRkYrERkZi8jjLZjk8zr/\nZRGOM1jV91rM3NZGuBYI2HJUWwchHiz3OtIB6MuMGRH27vUzYcJSRElfp9raTNVVr7MjkfVkEwKY\n5iPgSUg2ZI/yUVsLVVUtsazdeEt7QqGbuP/+XTz6aA8ikXMRkHmbasNezjzzZf72t1HU1hYga00h\nEt3RTj0jqvpOgxS9FjRC1oDTkDViD6JeX4XM054IsFiG4/g4cgRKSm4mKekCV/30+eoSJJwwnzPO\nuJy//rUxsZgmb9Hh8vq8nIQlird8DL1792bIkBsR481K9Bltx1nEwoX9kXnsLiuRMcvAeGzvR4Db\nx4hM3Y6AqOHqs/nqui4kego//3wo48btYuLEFxGvcUi9808IcOyDmetg1r8FyivdD1mntDf3Vwnv\nyEFCuh9BPHUdEFA2DTiPWCwD2TvTkH2uJ7Jm5iHr2KckJ1eyd+8F2PZmkpPvxee7DdveDOyirq4J\n1dU+JA9qX6AWy3qKH/2oFYcO6eMc+hhFAVlZ0/gulRNqh5ZlLUVMoemWZVVZljXEsqzhlmXpc3IP\nIKft/2BZVrFlWe//E+t7qvyHl2+STPy7WGzbZtiwWdTUaAvSGmA1oVAPZs/Oa7At7nDK7OwOXyv/\nHrgTt7qtjqABhRs0tW3bFsuq7zmwrD/Rtm3bo+GPJSWDkGTgiV4zCdU72bFJzCc4Z04+w4bNIjd3\nH7m5+8jOHgUEGgg9hbS0P/P001vIydnIBRdM5KKL/ouBAwNceOFuMjJGUFy8A9u2iUY/wIT33QM8\nQzz+P0hS7/aIRd6THEwVsVWZxOggG8bXSyavr2kocf3cucPJzu5Aaen8o8lq33svTGnpDLKzOySE\ncV5GkyafYJQuXfx89tnV6u9i5JxVdwTEgTfPmPu+ELIJr0EUulpSU1ezc+d0MjMz1DhPw3HuRUBZ\nDrCLcHjlMZlcfT4fy5aNIxA4iCjFOmdVGbIhB5BzZn9IqM8O4G4+/fQnxOPPIEpRISaEUHuEcpU1\nfTmBwALEmq4T8EYQ5W4HojyvpEmT+XzwwVQ1R7QhJVM9S1ueByPgNkA83oa5cyu56aab+OijFWpM\nzmPDht+Qn1+grPsaMC7DhFG6Q+t+o+oxGJMoHnTIm99/mKSkA+qzLxGw82d8vjfVs9/BhNeChCMO\nxFjrb0AAQRxvqJVbxl7H5EHT9QoBz2LbfycW095CXc5GtvNijPK0GSHQ+FS9f7bq67sRubka8RDq\ncdR1SQzPbKau66WepUPRdpGWtoR3352hcmb9FlEktXX8oBqftxGl2J30HPW+WxADx0T1joUkrl3p\n6YVkZWWRlZVFaupqjBFgECYMOFv1lc4JNx9REHciAKsPsdjeo7nEjhxpS0kJDBhQxeDBsxBFUZ8Z\nugnjtUxCQMHLJCVVqutyEcNGunpPWI3nCEy+x8HAEvz+17EsvTbZCHHMEeSMVk/Eu5GmfvYzYsSl\n3Hbbhaoe2muVjwCCrupdlyMKfbaqnwbrXyAeykxkLm1G1kvU91Wqf9ur/u6jxqwfZo6W0rDXoj0S\nPrgOAYUPq/5ZR0PRKpblIxwO89hjG3Cch5Hx1yGazdQY7VBXVyEyMgYBGhr8ltDQmuc4Onm4LrIP\nXnPNNa66zkfm2R+BVnz11V1Y1nLVn1cj8jsO8bzpZ1kI4NKfaVAyCAFvmpXVRowwE9XzeyTUsYR4\nfAxGNs/D5OrcCPTl88+D1NXpfII6iuPnwE0qP2ch8CwVFTeTk7OX1q27EYslqf7Qz9oHjGbv3gs4\n5xy9HmjjVqV6bibiIb4ACU9+CFnXr0VU/O+pNi1EDE5P0ZCn0HFs0tJ+gsjrS+pHh4v3RIDj83jn\nth47H7KfRZD9130EQEdwvIuRQ0s9R+e8/RDxpv4II8/aaNEIMTLsoa7ORyz2PrY9kLq6QdTWPssP\nfrCXurpxCBDWod+9gfNxnM4cOnSJGr/bgEosazc1NbWUlJTW64N/ZTmhhuw4Tj/Hcc52HCfoOE6a\n4zjzHMeZ4TjOTPV9nuM4ZzqOc77jOFmO41xwomeeKqfK8crXTSb+XSwmfNB7xsay+tEwqKhfvu65\nPZ/Px7x5I0hP9yZjTk8fzrx5Xu9ednY24TDIIqkTb99FOCzfNXz+D4zXbB+zZkW/1tjo9mRlZR2l\n1dex/Fu3TmXYsFnMnp3nAfMdOwqdfEnJMKLRdxBFbAHQG8fpS3n5dIYMma68cDqMSJcSxDqtFXMf\nElpyolDWYoxV2FtOnEzefcbGm7he90GnTp0YMGAAnTp1OjommZkZzJw5kBkzanjnnRasXfsMjRoF\n673HslqSlvaaes/ZiGKxDtlcZyIbUhQTKlOEsaBra/IgDh0aQElJCVu2bGHnTh2Wp+vdmlAomSVL\n7jkuk2t2dgfefXcK4XAyfn8LxAKrQReI7I/AyLtb8emmvv8d4nlJwmzaewBwnK8444znse0WyKbd\nA9loNTfXPsRD1IQvv7yBkpKSBEPKaPXsBYjX071R96Oqaj5DhggddqdOnWjfPotf/WoFCxe2oa5O\n98cUxKOyCVF6CvCGaE1GzpXUH6t4/CDx+NPqve2B94H3se0hxGI6fOsRZOy0zGkFJoSErraivoEG\nRDEaSHLyJ6SmbnddYyPKzemq3fcisl+JyMe1akyeQoCgT/0/BwkB3KnGx2tIkHlXi1Hm9b0aSFRg\nGBR7IUrqL0lLm8fmzVdRWbkKny/GPffkkpk5m0DgcizrYaATPl9jxPKvz+ckJj1/HQnf7af68YcI\nUBisfiqBCmpqIpSUlOLz+Rg/XiuGzyHK3GAEgKciwGANRjF8DvFAuMGqBi7TEZBUpfrsJVf/W3hD\nuDOALOLxDQigLUcAWGJou55nOcCdBAJV/Pa3EVJTX0JkbBVyxlCHhK7FnP/rA6xhxowC5s/fgYz7\ndmR+XIMQckzEGwrdUl33OgIuFyJyoUFlNQLCmiNK8SX4fD9FAOuL6hnj1W/tOQmpeiXOiRdVey9S\nv/X+dznmDBvq9xaaNVvFrl27qKnpjMjgdYgCvhbxGt6OeAnnqnfqs7N1rudUUj9R/CwMmDTG1DFj\nriA/fxGRiA4x3YEYbJYD/VRo30T8/nHq+xAydt9HZKgc2KqeeZl6/mRMsvMYIr9rMeeIn8VreHMX\nPzLfxiDn9KYia+Nu5Ozo9zDrqe57C/iRSglRioxnP6LRnuzfX4d4j4swctkV+Bmx2HIikcO4gYms\nLwtV/72HrHVdkHGbjcydBxDypGcR+euAePHcIBdEDpdxxx1bEdlpjMjx3cjcQLXlIEbveAvv2GWo\n92YhURIFqk/1OfFmmBDvAlcfayPrUGQtWYcJd9XniB3VnxayVvVW7bmDQ4duQdawHyNjqT3dz6h7\nR6t3e3WPoUNnHFMn+FeUfx+Xx6ny/6p8ExKS727RlrVsLOuf25asrPYNeoEaOnO2fPm9ZGZCKFRJ\nKFRJx46wfPm9Cf2deH5CSrt2++nXTxgKi4qKKCoqOumF7XjENxD1gPnZs2+juvoaxHtxMcc6H1hR\nUUEw6A7lcBetQLVCWzZDoZX1vL9eUJZM/QPXDZ8n0wyoY8Z0JjNzpAKjz5OZuZDlyx84mlfnWH1U\nXLyD7OxRdOlSzfDhjcjPX4TPFyI9XVuqzfvbtl3P0qUjycxcCPwZGZs7EW/iOcg5q3fV9cM5liUV\n4NVX13PZZfepUBfTn5CNz9f6pOZddnYHdu2axbvvDiYtbRVeEgQQ5V6fdUs8G6Xfpw/968PymhUv\nlQMHlhCL7cJ4V5MRkpBnEMBzBfAM0eiHR8dh165cjCKZi8hNU453tlQTCnkJebYjnqEbaMirEgis\nIDX1JoS0IVG5KcJxfo45k9RYfb8WARYvIQArF3NWrQVGSbkGUUxWIuO7AHOGTyt2VdTVXUQ0+glJ\nSbsQJe0JRA50W7Xst0Q8PlH12ULM+S45TynKoSaHwfWefcBNJCXNQxQxrcxvV20QJlGfbxtpabeR\nkrKSUGgK4fDTPPfcUwQC36dTp9Hk5u4jP1+Axty5Z/Pee2N4//12vP/+9SxefBeBwBHX+LhJgw7h\n97+BKGL6/Fx7EgF6efl/06fPJLZs2cKtt3YnLU2TcryGhF+9pvo9H/EYRFX73UYId5mKyI72BHVS\nn49EFO6ZGO+CNlYMRxg8SxE5dXul3Gup3hNCQAEPPVRCVdWXyPxNUuOQhIA+t9zuAEZTXX0Bu3df\niYDWCZgzR73Uc/WZ2UxkbuUgZDq6vrp/O5OU1Ik5cy4nLW0rAmw6YNsfIuulX/XBIkROp2POTd2D\nzItUZUBciCjjaXjD6kDCLb9CZHQ00AfL2k1VVU8eeGAxMq76uZMRj38HZN3vo8bgZxiDxyvq+8UI\nUHSfp9Ven0TCoj6MGbOcbdt+hwBaTYCiPWC6dCAWOxsvSct89ZMOdMXnq0BYYfMQY89fkDmqvcsW\nYgwqV3V501VHAbOwHb9/JgJqzkc8b9qocBkS7TAQWU81MJmKAG8duuyWjWIkNDSOMW6+oPo0AGRQ\nXb0MNzCR5w9DPHN5mHOTIGtPP9fz9ZjaCPiehPv8mWXdT03NaqLRC5A16HfIeB9B5sTziHfvDmSd\nb6Hq7+6XWYhMRRDDRp5qq4P3nCbIXJuCzOM3kSiPO1WbtFFIE5qAGAV+hHe8tyN7STLi1d6l+moH\nXrIhve7U11n+lWR9ieXfXVM+VU6V72RpmLnyxMQm30Y5lheofh3b8+GH09iw4Uo2bLiS4mJDNmLq\nD8dimSwpKSU7e5QnbLK4eEeD73IXYfdqyFpp6u8G846zF6/3on5p27Yt7doV4u3vTFJSdNiNVqB6\n0LHj6RQWtqzn/dXhvh07LkCUIW+4rGV1ZfHiO+ulZ9B9oJXVmTPjR58NHLeP3MyeJ/JWavCZnd2B\nDz+cxuLFXUlNPYxlTcYc3NYel1HI5nQLsnl75bBNm3U8/HAhtbVv4A1vk++1nNq2fULAruXt0Uf7\nYVmaBEH327OqHj0QxbMhkgUfYhXtglc5udz1uxdi/b8Y8dC5w4nuxnHOoLS0lFgsRm1tneu52cg5\nluOzrC5btppt27RlVnuexiOW+SCJXpVgsJo5c+p4+OGBiPemC6IgPql+JiPnyEYhCt9c5EyGD1EW\nDiOKxEeIl+53iDKYh4Dw3QhYHaz67VJEgbweCcebiihvm/j001Ti8T8hwKqqgbb6EO/XTYiSZCOK\nzBWYcXoaASw5iFLvViAlQXA83gJRqnoiitVY9f+5QCW2PY6qqo7E44ux7RZ8/HFvhg6dQbduT3jk\nu6RkGlOmbCA7O5tOnTqRnZ1N375dmTEjD7/fDYwlFMzv30MsNg2vgSVRwRKgU17ejZyctzn33P7U\n1mrQpkN016o2TlBtWoYoxT0xRohMhGEwqq53e159CAA4jCinP8EQ50iYsCikIxFgNg4Ze+091aGy\nI7GspaSkrCQYHE802lh9txaZq9diwg1rMVQH2mOQhwCcfchc0KHh2lNVioCn/aoeLTApQ/TcMCUe\nt6murubgwZvVJw8jZ/6mYbx2tyFgRs8RTdh0LRCnWbPDjBjxvvq+F+L1095DN0FTAPgfYDmO05dI\npCfV1Q9iwAyqr7apvrsbI886THaPavcAxON6BV6CnGWucTHGVNvew4EDXTGAdhACgt3eTF0sdV1n\nV5vNHmLbbVT9ZiLz5R117Rnq+nsRb88fkXHSJD6DkbXwWSBELHYWMqb6rC14PY42XmBSiqwP+xHP\nWWLdmyHreTIiI5rdNYbM/xLqA5Ou+HxnIWMzlvqGzExkbRqBCQ9NBMxRHEd79vtgCKZ6oXOR+nxb\nkXFz92UnYASWNZhA4EnVxl7IOHdR/fuVqru+73Zk7EpISjoPma9vIx42d2mP7H86ZP1KvF5Sfaa2\nJ8bzrnMNTseQDbmjEr7b5RSgO1VOlX9C+WeeBUxUsk9G6T5ePRvyhDZMz29YJvXZq0QgcqIQhOLi\nHeTlLVS5pU4MdrOyskhLK0I2jSqOdT4wOzu7gf6+mzlzBtcbg3nzRhxVJOuTfbRn9uzbCAT6k2jh\nDQQGHs3lB4lgrBtHjjSnpGQQkyeb84qJ32/dOtAVIirezWOlgkj0ViaGt7Zr14o1a+5h06ZfMm9e\nM1JTdQhWhqveLUhNhXA4n0aNVh/tg169mhGJ9KF+eNsK0tIGnhRgT5S7vn170rHju+r9TyM5w7Yg\n1vRmCCBrOBVFaupc6lv1HcRKrdkXeyCKzjYM0Girrr2AvLwQAwY8hTesVnu0djYoO82avUY0GmXM\nmHk4jvv9mvygF0bZN8ph8+a76devHxkZmciBf23Vbql+fqzuexoBZ9dhrNCPoBUSUVrcYYSzEGu5\nfmZXTA63xur3Depd0xBF+2dIyNb3EaVlc0JbbUSp24ExUryFeC21nJyNKOwbVN2uxkvVXYyx+u9A\nLO19MDm7ZqrxqSYafYFotDdHjrRj27bPqKpyk/ZAomW7uHgH558/kqFDXyUWG4qXyv9ilTA5U9V9\nOsZTqYv2juUB7xKNhqmoyObTT29DZO5mjEcgR9V3OXJm6TcYpXkwErLVXo3LcDWG7rBCzXi6GGFK\n3Oyqy1685xw7qPEpU7+XAVtJTf2SRYv8zJwZRUKCdQoHfV8/jHfiT4iyuR1R6q9CwMAixDPlYM4S\nrUfOXmmPkj4zexYtWqTg872CmRva+7oBKOfBB/2KwXgLYji4UbVpOWJE2IuAFTeRj/ao3Ud19fO8\n/HL50fEV5f9LBNQOQow4E9Xn+ky2rZ4xFq98FKk2TVTt1KGdhYgyvxxZAzShuh93mLt87h4XAJsf\n/vAFDDi2ERDQGC/7p43I3UcIsGnparNmquyv2t1KvW8wQvzyHAJWtQFoOEKS4g7T/QGyFs5HPEKa\n2OhHCECMYVJytFVtz0DAhSb6WaiumZRQ9ywESJ+u6j4GwzhcQkNgXsuBbXdR79LnJvORMZmFgOow\nsvYMRp/DlqLXxAzXM/3qup7I+jcE8OPzDUEMBAV4xyaD1q2TGTvWUUynIGdHmyCycwMGTL2o6qfz\nc75AUlJjxJjyMiJfizBr7UuIvNjIeP0VY7BaqvoshIRIP41JYXMJXuZRHZXwf2+g/zrlpBKLf2sv\nO5VY/FT5f1a+SULy4xWdeFsnBm3W7AUgxP79Qumdnl7A3LnDv7Uzh8eqf1FRUUIydCkNJQt3P6t+\nUu5cLCtKx47vMm/eHQ3Wu6hoO507T1DnonTy4RswTI13H72vofp+3TE42baZ63Si4VxE8Slg4cJB\nnHtum4TvuwBgWS+waNFNnHvuufTpM4nycvGAnGw/HksG9u07j9radYhiLxteILARn28AlgWpqasZ\nP747ffv2ZOnSpQ0kYi8GXmfBgnMYMGDAcZPIJyao1XK3a1clgwf/Xnkd2iObaGtMolxvQvXU1OU8\n9lh/2rRpzsUX//4oU51hfOyFbNQLVB36YBIk28gGPt1Vxy2IEl6l3luEKEaNMOcstIKwlKZNkzlw\nIIooaTphNchm/zGiyHgTScv4daVv3x6qj3IRBe3FhHqUI160vQgoCyLKUAEmMe4+1+9u6r7fqefo\nJNru/rhXta0FAhTbIAq+7jcQJeR3iOJ4A2K5dtS1ut/cyeVx/T9SfWYjXsF+ru91HUchIDKg3qVD\nZIswiX5tdd1ATGJiU7R8S27MUWzdOlA9vxBRrErUMyZjxhv12SoEHJ2GyEUx4tXYgGFT1XWJ400s\nrOvVH/g9EiL4C0SG7kaAXymijN+FyPAMRI43IyAwHSPTWjY6qzEYquo7SrVjNAJCfqfuswiFNlFY\n+CBlZdsZPPhV4nH9TN1HWxBgpslVXkWU9PsxicbTEaX+HWScByFy8RHiOdTjZiPAbiaixN6g6n0Y\n8RrrhMmocfsS8bheihhSdiNzYD0GPOp33YKZU5kEAk9i25uJxZqrftTeYAuTzD1N1fFcTC63NXgT\nNy9GZKu9GpcU9b5s1f4ByFxzkNDZszDyr8ddJye/TNXhD/zgB3/nq6/aIaGiM1VbT0c8QKfjZdBc\ngICBeeo9qepZLyJhffuoP6dtNRbvYcb+aXVfa3VfASJLer7sQ0ISdXsnIXKg275fPTNNvVsziO5V\n1w1Wde+ixuF5hJAkhIDHADLOd6vvn8E75lpOixEjTTNkH6sE3sTn82PbL6trRyLeweGI/Oi1CcQA\ndjNigNCfRUhO7kFdnQZCj6jn3oMAxctU3y5k/vy+jBu3gv3740hkxEbEKDVO9dHrCOFXn4T3xhCP\n452IDFyCeGd3IvKyC8NIehgxnB1Q/ZKDycM4EZGX/0HAs5Zv9/6yA5iOZV1MSkqAcPgd5s0b8Q/p\nWt92YvFTgO5UOVX+TYoXEGkL50gMtT24le5/5tnDbwLo6t8jICIUeovCwsvp1KlTvXvMvdvp128a\n+/ZdBeylceO3efTRYfTr1+tbb2f9fpa6JvZrUVERnTvvpaamELGmz0I2VodAYBFz5vQlPz9Jfe99\nVjicR6NGjSgpcW+wJx7DE8uAnM9ITR1HKJSpgwcoAAAgAElEQVRGRcUsz3MzM0cya9YgbNumS5cn\niERWe75PSenJV1+tpKSkhJycvco7YkootIrCwhbk5y+q1z+ZmSNxHIdt2yxVn2K8gOXY427bNhkZ\ngykvPx2TxuDXCH32aRilZRNiVe2PKL67Mcq6fvZtSKjRLzCAxa24lAGtCQafpLa2GbKx71bv+UBd\nf72qw49UW0AUsFLatHmJ0tKlrj5qiShY7r4qwgCutxClIhkBBd0QC7xW+gaqdu1GFMc4orCH1buv\nQqzO16hrDqvPQogHwd0HbnnQ9XgZsTy3TajjdoR44UdISNM+kpJeIR4/B/HGvYIotj5EYRuCyUOX\niozNZaqN3TGexFsRQBxEFNdRHEu+i4uL1ZrQHFHWW2PkpAg5G7MDo7CLUgWdsKw3SEr6DMfJIR63\nEC+BW8ZGISBiIl6gHsQA4o7IucNDiNz0RBTWAHIuSns2pyFey3ewrBwcpy1GprUxZLd6/vMIKJyE\nGWt3+7cTCDyAz9eXSOT3GE/gdNd9Ok/mCPXeOKLMXoucedKA0ocwyUbVmL2KUL3fggClVxHPzFWq\nTRsRQFSFAAvdhhgCzLUHylJ9cjsGtLdTfVWNzI9qZE7uR4BKL3y+N7HtvYjH93NEZlarfj4NMS58\niSjWP0PAWm+Moaez6r/rkPntXudWkZT0LPH4+YgXbgwCDixkvnRBAEMzBNBqMKv7YDZi6Po+Ao6r\nkTnxBDKnHCSM0W2AiCJza5Ua449U3/ZDvPAXq3Hpi8yJ8xFvoh7Dnur5fkSez0bCDLVhRRsX9qjP\ntqh39HLVfwWGkGgAArS1kULPB1k3Gjd+k4MHb0fk40FkbQljQPQMNQ7bETDTEpGBLohMrEXOu3VD\nDCQ5GOPA7xHv1g2IgWw3Mv/3I/J7sap/RwKBZNLSiti/vzeRiE4xcQEy9/aqcd4LfIDf34zf/vY0\nHnpoKxKl8LC67h5V36/U36ORfdZr/JQQ90LV53ci4PVm9XdzZH16R7UlhqzBen3sp8aop+rflarP\nk5D9xy0LYggLBBYxa9bPGDBgwD+se3zbgO5UyOWpcqr8m5T6ZCL/2EHdfyRUMysri3C4fgjb1wtB\nkHCNkyHgEMr/mWzcGGbjxmv46KPXGDDg1pNaUL9uO082XNbQo3fGm7y6B9Hoczz00Os0a7aK+pTa\nPqqr0ykry6WhkEed5kC/y13/oqKiE8iAD7iQzz67mupqd34ogFK2bTtMbu5HXHHFx/z4xw7Jybcg\n3o7HCASuZNasQfj9/oSzaKbU1taxc+dOFSYKoqRIIvqdO4Ns2/YTV330WblEJjz5rl27/UeBv5uk\nJzm5EFHoYogCo/OwVSLW+JXqWTocM7H8mDPPvAFzoN2n+ng0sIdQKEA4PBXHuUiNzQYkhOsnGKKN\nDurZ+xFFfyrwOywrmf37e9Op02heeaWASESfddFKp+6PDohy70eAUXNEaVuIgJQ8DMnKNAT0TUVC\npHSbOmA8KznqudNUf+hQPPCGqq7GnPkpRYBge8RSrdlRwSjQZyCgrT1QRTyuCRP6IWDvLsQLcD3i\nSXhQ1UcbbNxMdOtVW0ciYEjn6Tq+fEvJUu1LNPj6EMV/FDp5sISDvYnjWMRiVxOPFyCgyUm4T5+D\nnIhhxNzvqlcSJgfgIERJ1uHdV2LOOz2JKM1tgCE4TiLjqQ47O5fk5BrFZLxN9Wkl3vlvA7OIRp8j\nEmmDgK8CxKtwl2qfJtYoVv3eCnOm62pMCOI6RA5eVu/SydL/qJ61FhPimKHaPQLx0iT283IEIPgx\nXhEdxnYLhkxnLgICNiEGiVTV1onAJmz7BmA4Pt8Hqt1/UH3TWrVzGmI8aI6EBeqSgXgFX1DtfI76\n6+ZG4vHJqm9uQIw2kxFP1KeIjB/BS1C03tUHAQQQa6NG3NUmC5nn7vfZql903lV9ts2HYa1MQuZZ\nsvq9CQGJmaoPRyGAqgiRs0oMCYieG4swya+zVZ319z0wJDlDEEBe6HpGe9Wnl9Os2Xq+/NJGwNBH\nyJrxKSZRuGbt/bPq/3MQT/hUVedK9dkDCOhpiff85lvImCchoHmq+r0FGf/t6Lxz0WgzamvrsO2Y\navdqJLx5j3pGK/X8JsTjnXj00TIE2HZA1sXrMCzIN6k2hvEWvd5W4fdficydWxC58iHe8jgCHC9H\nxvgjDDGXHke9z72E7C23IKGZUzCEYjpEuZpoNIdHHin8zqUsgJNPLH6qnCqnyn9QSQzbS09f8LVC\nNUtKSqmpkfQI7iTZc+eOOg4Jyz+WND4xmffJlG/azsRE71lZ0+q1S9OjDxhQjpfQAwS0/ZyxY/cy\nfnyU+oEJbqOcSZAcCr3FkiUmjMNdf8exOeusGdTV3Z7wrPog1bYdvNWVcy+OM18lJV/DkSNnIpuk\nsFP6fL9kypS38fl8TJiwRCVL1sm95RmOs4oHHnCIRDRldxf13Xzq6soRRdj0gQFSLRQT3rUEg8m0\nbVvI3LkjEgBye+bMyad79weoqtKgxsIoLTIWgUCIFi3y2bevDbW1OzEKl7ZWX8xXX/0Yy4q5+t0k\nqZ8162zath1Dbm4BZrMPI0rwr9T1WtEHUVieBRbiOD4iEdi6tRvbt+dgzsFMQxTsy9U9TyLAaiKi\nwIxHvB19EEXrSeAakpKqOPPMcg4dupt4XIPg+Yi1XM+TDCT8U59ha48oLf0Q8PUVxor8HMa6707o\nHkas3CMxHtA7MYnBp2OS0bsFx0E8LRmqjTrMtSdCzjJBtXGAqu/bGOv3KHWdViQfo2nTV9i2rYBQ\nKARow9B8Skq64U1krftiHiIDz2C8AzMR4K2923mI8rzEdS9ABMvqg+NoRkx3vbojYKCX+kyf7XRT\nvnZFzvG8q97TRX1+GMsaiN9/AbHYYOBaAoEkLGsFkcir6ppioAVJSSOJx1u5nulOiwCigmkPXBtE\n4dRGHu3lsRFgda6qy2mIFyIZSQ9QgoDQNNVXmt7fPZbZiNHpVgzBxXRMcnN36aD6KIZ41AqRULRb\n1bsy1LtHIwDgCoxBS+ahbXdE5qNOXP8Chl0w9X/Z+/I4Kasz3ecU1VVf9WS2ZEZMQrcbVUgYKTqE\nmHElGU3iEkEj4AJRWTU0blluzOQqAgYwLpDf3BtBBZpumt7Qq3MnmZhFgkbbsoCmkbCpUTCJmTuZ\nGSczQlNUv/ePc06f823VVd3VC/g+v1/9oKvqO99Z3vPVec/7nOeFyVF2nSpvLWSUTKq3yv6wx0L3\n2xuQTs4bqr+ehBzDFwH8I4Q4A9HoJcjl7oR0NuxzoIB0El+AdLy0hP8umGThNZDzJqna9G+QdqDP\nKEL1gxbSaVL3uQPSBmOqPn8C6VxqBsRYNSbXW/17sSrvP3DbbaPw05/Ox+HDn8fx46chEpkK4EZE\nIm/h6NHxMA79RapvxltldAN4En/8YxeOHbsKhrJ6NaSD0qLGaizkZtBHIOeC3rwATDqZt62xBMwc\nblJjNRFyU+qQ6qMlqk16Thq20OHDMxCNfgbSkdPP5/cgHa3LIDcj5DP1+HF78zAKs0kVgaZ0R6PL\nIcQm5HLXwkTNvwDgo8jnT4WMqL0FuZE3H9Iut6uyTlfj9y6kI6lxrRo7nRKnDdLWZkNG+WZDzkH7\nWQocOHAtZs8eeCZUqRg+NWEwGAXhV86sQZDQQ29RsjBlxWJzqujrDxzYoM48jQZREomEg3R6rO+7\nOrIEYFCTxve3ncWkzjj77LGIxX6KoKS5kUgEX/jC55BM/hzeMaqqOogxY7ahUNTKXf8xOHLkWRw+\nfCpyuR9a16XhTnC7HcCrEOJlT/laCnsvjOrcY5COQhuAG3H06LXo6HgEc+ZswMGDX4eJjJg8hUI4\nOHy4BfLHUUckr4FcDOjzUrZNSkn8ZPIVvPzyQrzyShIvvnhmYP5CnUPu0KE2yAVaUBSkBp/4xJvY\ns+cxvPDC36GqSisY6ujNash8UleCqA1eZ1en25g4cSLGjDkESbEkuHPm2YvuCOQC4xq4fy53Ip//\niOqjuyB3eVdb/aFpbDeqOnwOciHUosbhdgAViERexnvvfRn5vLafiKrHe5ALwGYAexGN/gjuSFwU\n0vl5B4YuuBJycf4LmITleiG1HJKqtxomKbZujx3V6xkNSPs4V32mo65R1d47AfwK0ej5iMX+XqnN\nvQF3zkMdmfsW5KJsLN599258+MM3YPNmmd/M3hiS514+g4qKqxCLNSMe/z5GjNgNGXmZDWlbv4bJ\n7bYLcrf9RfXZfZALuRYAbYjFvqlSmdhRbHuT4aOqL/Tnt0IuhvfB5HbcDyOpr8d1A4iieOKJj+CV\nVxbhlVeSeOKJHCIRW9gkBuAJ5PNnwC2pD5hnhY5ga+ENHTUE5LzRkvz7AUxQfWlHrH8Lt02cpcbS\nVozU9wDkPLkD0ukfA8eZjoqKM1Xfd8OtbqgTzl8DGW39FOTC/YeQtr4D0pHeCaPyqqPUayAX15dZ\n5UxR/fksZJLyL0I6i92Q9MevQNruryHn4LWefuuGdGQqIKNLr0LO3XmQ4z8ZwDdA9BsQjYQcr4/B\nOGlbVRnnQEaaHoWcA/MhI9cft75zBNKWboG0q/8LkyJAq2k+DUnz/DWkU637+6eqfWm4Jf/HQVJE\n9f+NaFUiMQVz5lyPffvW4oknBM48cy9GjJiFSOQQTjnlZ4jHkzCCYNpWdBn6TOkIvPfepWo8roN0\nQrUTrXNe3qvGzlaMHAGj0BqBX9hHz+F2VXf9fPoPyA2YD8EkvfeyhaKKmqzHT2+SbYAcS6042g0Z\nIdT2VwO5ieB+dv/N3/we69ffgoqKL0JGkddC2tc9IHpJ9ctm1Td3QG4uXQQ5nv+g2lUDGcm2y74A\nkg6qNxzsSCwgn+F6Y0D/xu7sSXkznMAOHYNxgsBPBXwayeR7SKVuLclBKpQHrpgHlPt6vdCehIMH\nJ7uutyX9tUoiALz66iNYs+YI1qw5gmz20QFLGt/fdvYG7YAcO7YKQTnrUqltyrHugjeBuxBdePLJ\n+QWdW1N/IDhZdBvkIv48SHrYTQB+AuA5dHU5mDbtrJ7yHednkLQuO7G3Vlm0f6iacOSI3ol9HTK6\ndLp6zYL8Ed4N92IFMItY7YzcDOmMbEJ19S1obr4X5557bo+zqnO/2TDt1REKTUm8CUI0wnFae/oo\nGo1i0qRJeOaZ+5BOA7HYC5D0NN2WxyEjRzqVwErE49fgiSfmIRKJIBKJYP36W5FMxiAdrW64c+b1\nhp+q/ngDcpFtO3zdMDRODe3cRaGVMoGXkMs9i66uz8G9yNA0sD9g8eJ9yGTG4KWXViOR8Oa6GwPH\nicDI3VdBLtDs/IP2AjsKEy0YAbMhtAVy7OyFb5CTpyEXk9Hom7jvvjzOPrsaI0aMgFwYk+d7D0E6\nak9DRjKn48iRNsyZswFHjx7Fddc96toYAj6LsWOlqutf/MXLyOfHq3I3QJ4De9m6xxuQwjN6of0k\npDPzFoAn8a1vfQ6nnKIVMb31WoV4fCSqq39ofT4O8vxOGtKJmgrpMHupyxEIcRnGjh3bo5YLwLLn\nXZC0tVshozm2pL6m3dqUuzrIxbeXmhwFsAgjRjwOuTi9DO6I9eWQzkYacsH/Cxh6pO3I6UX5XgDn\nIplsQ0PDafjjH1vw8ssX4pVXFiGTGYtlyz4Lx7kWclG8EiNGZFFRsR5yHh2GnB8VkPb2Bcg51gCZ\naFv3r94M0VEW7QD8H0hncAPkfIxC2uXfQzpOet7+BCaPpu63VWpcNsAIwcyAjMTYVPezIXPIacn8\n6yBtx+6D2ap+d0POgb+BpAk/B+n0zFT9e6H6jo7Ya/qd7Yx9TL0qVH+3wU0T1SwM/WwFhPgnuDeo\n0qiq2tKjVv3QQy/gwIG1OHJkGo4e/QYOHfohIpEmSMfzPUgKoq3yug0yirxYlXkZ5DxyIG1BUwa1\nau8lkM5UN4zS43FIRy+I9izbG4t9FBUV+jduLGRU+TxV5o8QtKEJACNGXIFYrBHuDaYIpAOXh6Ey\n/gbS2b4K0kE9HULIjZ1EohXJ5PW4++7zMHr0R3HqqQnI3zDdzxHVP5pefQdk9H2tKvsBNUZdkI5n\njXp/NeTvVDdMYngdiX1J1XkqpCMu4M7L+TaOHv1H7Nv3ZmC7hwrs0DEYJxA0FVBL2e/btwF79z4W\nKm0/VAiLjl133Up86lN3YcGCSixYUIlJk+4uKnfdcIRxQLSkuo4OmHNCu3btwjvvTIWRTT8dwPfx\nzjtTEJaWQEc19+7dC/lDuRPuHVCTLDoafRfxeJWq0V9A7hqPBfDXWLPmJbz66iOq/M8imXwW/nMp\ngPuH6neQP/B2JOMtAK+jquq7VvJ2bxk1EOL/qntvgKTzdSGZfBZvvLEeNTXjAh388LE3ed/i8XHY\nsOF4YGRP51J88slPw3F0dMNLazsDwJk4duzPsG/fvp5r0+mx2LTpdtx//9+iuvorcJw2xONnIR7/\nLoSwF03eSPhuGMn4BfCfSdoOuTP8HUhHPw29oDPwRgG90dC7EI9/AVdeeSUmTZqESZPSeOGF+5FK\nzYfjtKCysg3J5I0wjnVE/f8FNQY6uqMXUhNhnAV7sX8h5CLqF+pvmSdNOhkxyEXxK/AzAYBPfOI3\naGs7jM7O7+PYsc9DLqq9GxtNkItk29ndiSNHTscZZ1yNAwfsc456Y+hiPPnkRvz+9+fAHVmrhDwv\n9SKkg7MNJnm3tpczIcfbwdKlB3Ho0ERIZ0A7UQZjx/4WTz31P5BMft/6fBJkhO5DkNGk0QhCPF6B\nSCTSY9Pz5zsqFcsuGHVPOzqjnYAzEYt9FqnUfLWRsw/jxwPLln0SqdStiMdlxEyIRlRUrILj3I8R\nIxZC0mrt84o7YaTi71L1fAfS2dKqhLYj92kkky1oaIhh374mXH/9tdi1axcA9OQDvPzySzFq1J9A\nL6jz+SuRy02HcZquhYzuODCRnCjCc5fpTQo7OqU3i+wzcjqK1gp3Trl9kOfqnoPcrID6+05IpyYo\nMngzzPnH2ZDCJVdDnhs7V42PPjc2FeZMl6akfhzSufkppHM0Aian3FMwzlga0ll8Vn0XkPavnelO\n1Sc6D9/rAH6OSOR1VFffBMdpQTy+Co4zHYcOXYvJkw9j3LiZ2LdPR8o0oujuvhip1GokEl9CNHoK\nRox4R7VvparvZ9U9fwzjXP4C0t7sjSIBuUH2jKrTNjUGj0A6ZYAZy+PQbA/gVYwc+RI+/OEcpCN/\nE2RUrhsyUrsK7nyLGt0YO/ZlrFt3M2Kxb8JNoQWknepE9ddAztOnMHLkD1BX91c4evQprFsXQVXV\nj3Ho0Hm46aYf4jOf2YzDhz8NvwOplYrPgXT2dBTyOKRz9zCkwz0d8hn5kBqbdZC/mTfB5NibDPlb\n/gDMeWEtWmQi9UQb8L3vbStZf2BAQUSD9pK3YzAYQ4l8Pk8TJiwiIE8AqZd8L5/Pl+X6bDZLlZVb\nrM/ld4SY1ef7DnY7e4O/jXkCsuQ4KymTyYR8R74qK9som836ytyx4zWaMGERVVZuoUSilRKJqwnI\nEPAgAf5y4vHNVFV1FQH+fgVmUl1dHWWzWcrn89TQ0ExCNKrPFhGQI6BWvfLWdd6/M5RKzaCuri7V\nnzl1vft+qdQsSqdrqbKyjSor2yidrqUdO14LGAvZT0CG0unanrHoz3jlcjlKpeZYZbeG1HEO5fN5\nVz9XVm6hdLqWGhqaKZvNUi6Xo4aGFkql5lBlZStVVrZRMjmLUql5lEi0EHA5AS0EzFPl5wjQ936N\ngBkENKt7dqrPHiLgKqs+WQLarD7W/ZpVr5yr3fl8nrLZLGUyGcpkMj3/99vWayTELHKcZorHV1Es\nNtmqy2vqPm0EPEwVFVdRNFpLwGbPZyvU62pVt2cI+DwBMwloIqCBTjttFjU0NFv3zyr71O1tVmNw\nXsD9W5W9NhHQ6Ku/vE+agOWWzes+yhPQQcBFBGxS99P11N9baNmwtudOdX0LASsoFptMmUwH5fN5\nSqftz9sIuFHVWV87N8CO5lIul/PYaycBkwlYqertnUvGnnO5HGWz2Z656R3j9vZ2y5512VcT8LT6\nt1m9PktVVTPJcVZSPL6czjrrWrrttrspmZS2m0i0UDI5nRoamnvu47X9CRMWUTbbSePH637Tdlhv\ntcV+NiwkYLplv7adNxAwVX1vtRrnTQRspGh0omULzWrs7XFdZn2u53EdAQ+QfP49aN0zR8AU629t\nf3ru63boedVOwK3qHpvIb7NXE9BFwGwCpqnv2c+QvLKdOaoel1s2Mkvdr0X9P0fy2dBBcs5r29L9\nUU8VFd+lWOxL5LbbOhJC183Yi+OspLq6Oo9N5Am4W9VTt/tRMnPhNVXHB9W9m1S75pCxZ/0MaiLg\ne2Ts/x4CriDgEVXfFaod+vdC3386mfnbqcqX81qITZRMzqGNGzfT0qUrafToW8j9G9VMwB3knv+y\nTrHYcspkMtbvgT2OM1R9r7bqoe2kwTOur6k61qvrO1T7v0Xy+fGIen+Lek0lYJLVZ5vVfTpVX3rH\nJvx3vFgon6h8PlY5C+v1ZuzQMRjDAuZH3b/4Lsf1wY5MlswPdvkeiv2ppw29oLIXWYVQjANSipMS\n/N0Oqqi4kuQi1784TKdracmS7wb0q1wYx2LNrkVbKqV/zPXi+u6AHyrjFHj7TPdnPL6KhJhFQmwi\nx2np+U5YHxp70PeVP6JCzKKGhpY+jZf3mnh8tdowqFc/wG3kt7VWymQy1kJBO1BdlErNoPb29h6H\nybvozufzVF9fT7HYCmvBUKv6/hECvqTGyevwSqe4qupySiZnk+M0UyLRTI5jOyKyLCE2UWVla2Cf\n2wtw3ddBtpVO1/a0oaury7MIzBBQR1VVl9P48bUUjzdZmyx6cbRMtcN2cjYTkKZrrplG7e3tlM/n\nKZPJkOO0kFlgz/GUk6FzzvmqaqfdJ3rRPZP8zlitquMi9bm9+LTtZ4Wqk5wj0glrUnVfSsYJyVr/\n105HCwHNlErN8Til+j5L1JjqeuoFe5t6LaSqqmkBDnWWpAOkHYFOda12XhsomZzj2uQIe94EPz87\nlI2Fj7fXOfSWHWYzVVWXq75bTWZ+tpJ0UG2nSW8sbSE5x+xyclRVNYWqq2f6bL+6eiq99FKWhLjS\nshH9PHtN9dF55F70d5J02par+3o3aWxnXo9znuSiv8167xn1veXqpZ+VtvPXSWYjpomME2DPgVYa\nMeJKOvXU6SRtdIvnvlllW9NVORmSTkbQJpi9oePd6MhZZdWSEI2USLRQdfWlFIvpumuHVjvhuj7a\nxjeT+c3oIOlsTSHg0+R2wqZaZeRUOXPI7URN9fSnHu9mMr8d5tkSjS6k++9fTsnkLFUHPaa6nc0k\n56u9KeCem8nkbFq6dLl6vnid9RkErCIztzaTnLPaHvMknxu1JB295WTsupWkLf2dqpvbfoErVT/p\ncX+Y5JzLUPDvyfBy6DgPHYPxAUV/k54Xuj44l9urEOJ1EF3vKqdQ7rpyIKye9vtADHPnPu5LlN0b\nfdWoUF4MAEgmt/qSjRbzne7ubjQ2NmLevDiOHtXKe1pa/jxEo1kAHcjnPwZbVbSp6U4cP/4+zj3X\n7tduBOX+SqfvwPvvv4eDB3VOt27IcwY3Q9JVDBKJVjz22BFEIhGMGTPGJQyj+01TTSKRSK/28+qr\nr+LCC99EV9cvffVKpeZj7961geOixX2KtzN5XmXkyLvxL//y1UBbW7PmCObO/Td0db2u+kHn0boA\nQnQAuAzxeAXOPnubywb0GM2dW2G1A5D0t26MGrUU//qvs9T42QnUjyOZfA7NzXcjnR4bYHPSLkaP\nfh5f//oFEOJ4T58DKDLBe2H7u+66lTh4ECC6EkAOsVgbjh3bAlsdVIi/heNEcfz4D5DLzYfJlaXr\nuxeZzBhMmjQJO3fuwS23PIbOzvdApBMFj4YUqvgCHCeGMWO2Yf36W7Fv3xu45ZZH0dV1GyTlyc4v\nZycZfh1SMTCl/r8eJsdYFSTtWOd0A6SN6/yPF0FSQ38JSSHVibq9CdG9tncD3nlnGt5//8uWlRyH\nVL37DkwS+KlWP9TAcdrw+OPHsGBBpZVXczskFXEbpKLpvZBnB/Vne1FV9TTefLMVu3fvx+zZa7B/\n/0Ug+jWqqrK4995rMHbsaNTU1GDnzp0BeSC3Q54btPNwdfcot95www29PsNNLlC7PVFIkZ7LIM87\nbrD6aCfkecBn1f9/Dknr+wXc+SEB4HlUVf0af/jDbE9/ynm3dm037rmnEYcPd0OO9zZVxuWQioeL\nIJVTR0FSZ7dC0o9vg5TiF5BUOJOUOhpdg7/6qw/jD3+YiuPHfwR5FrMNJpfjG5BUOp2H8A71r1Zs\nvQySmqlzwP1S9fN/QlIAH4ekJb+JaPQZfPSjI3H48PWQ9FNNrbVzlUGV8wakHb2j6nEa3Lk4w+zy\nWdX/0yEpiesgbejHqpzT1WcbIamv4yCpl92QVNFNapz+GfJM4dmQdqiTzz+v+vYadd/zVf31ed+x\nkPau8xx+HHKujrXaoG3ndchz0vdD/o5UAyDE4y+huvqvcPCgPut2CHI+QdVTp3MZByk+cx9Mrkjz\nPAJOhVTS1HXR99dn/tph7EHP/9cQj9+Mrq7H1GejVfl28nlAnhPthvt3b7vq52oAGcg5fB8klV3n\nhFyLoOdwX0Xdyp2HjiN0DAZjQOCNtowfv5BSKU1TKxytGry62dTGvlNQe4vsFfqOrovjPEhuypF3\nJz1HyeR0qqur66Gk6LLd/Wrv/pqX46xUO556N1lS+9w0WB1NukLRJ91Rob72taS1hUfNvLucur8a\nGloK1iOM9hqPL6dkcnbgmLa3t3uiUuE0Um0DeowSiVZ1rXvn3nGmUn19U68U3EJ2kc12+iJx7ghS\n8M5wb/ZnaIWF7ENSu5YsWUKOo6NM7nvGYk099zBRniDKo6To5nK5njrU1TWS2c3PkYwUeOl19SQj\nnbUkd94fUtfkSVLmvkh+mp9NvQqiEbYAE1MAACAASURBVOv3dQTD3aZEosUTwcwq+59K8fhUAi4L\ntVkT6fVGGJ8mGdnS7bWj0s00evRsNV+9VLxN5DitNGHCIspkOgKeSRlyR9NNxMNxWnqdozrCXFHx\nKPmjcI+ofxsDyn+IZDRlAwHXkp9Wqp8leYrFlltRW/OZ4zRZ/dxFMkKj+7yeZGRG0xdvIxMZ1O/N\nIhkpmqnqeD99+MOfoerqmZRItJDjPEhVVVdQMjmHotGvkYyqdBFwMbnZCzoaOEXV4VJy0zg1XdRN\n0a2ouJiSybmWHeVJRvJuID/dvUNFItvJPPO80a0gu7SjllllF7VW+XYUXEeR7ejWt0hSI5tJRqUa\nVDmaUaAZMprG36b+tcu3o9OrVP9sJjO3dLRaRxM7SNKxa8lEbqeToYJqaq09f2ymTgcBF1pjYP/u\neZ/N9r/T1djYVMoWAlpoxIhPUjS6XNWxk4BzydAx9T1mkP8Z107S9paToa9myMzPqWRH26urZ/b5\nN1EDZY7QsUPHYDAGDN7FZn+pnuWqk3shFuwADSQV1D4vYxbb9o+ZdwFQuE7aaXKcZorFlquzcmEO\nnfv9eHwVpVJzemiUQEPZzjq6+/phKuYcgt95Cq+H26FzL5yrqqZRKjXPZ2uZTMbqH5vOE9zf/oW7\nplrWUSxWS1VVl/ScxyqFXuulcwZdm0rNoMrK/tmm3+kNt/f6+npKJLxnh6STk0zO7qm3++xc4frl\n83nrfFan+vdS8i/0ushQCjsJuETZix7Xr3vsx753huSCzl6068XXgxSNehf2pp7yvOQsZWvynGkq\nNY8ymQ66//7lVFFhn300Yxp01nL06JkUi00l6fzoM1becW0nQ+kLO486g+LxVWQ2DSTVU4jryL+4\nD7c1PV4bNzZRMjmHRoz4HsnzUfazb7nVVzaFzktvnEPA7SQX6+FneqXjph2iLQS0UTw+mRyn1bqn\n1x713/9AwAVkzk/alEZJ6ZOfTSMhvJS5TorHp6px1GcNbwgY9xxJCmKG3DRO2/kh670sVVQ8YG2G\nzSI33e9OAr5IsVgTxWKb1Nk4bedTVD2CxrmDotELrfrZzk+7Kl87fHoOaOfiXjLn2oLsKKfu207G\nucqTdIRWkTkTllH39NI+d5CkH+p66PZo+9PjYjuEul0ZMmcQ7c0V0/+Gbq6/G0SRtp/pD6txfVD9\nq+3QPjdo338jGee8kdwOXYbkb5F93dOqPy9TL+2I206++3k4fvzCfm9El9uhY5VLBoNRVnhzz9m5\n3Lwqndnso+juPort27cPmlpUcDqD8rEeer+/rfa4FZ2dWh7ezvvzHIzkdO/Qao8vvngWXnzx7zB+\nvFd+vxtjxhz25KaT748d+wZ27/7fqK7eDUlZOhtEdvJ3AIhg//4L0djYWNJYuVMv/BpSDc19/2Ry\naw+10lZHPXLkjMB62Gkn0uk0Ro36IWRf6eSv1wCYjsOHm5BIxPHzn4/CD37w37j77kk4fvx97N//\nJhAisx2E/fv3e+xlHID5EOKHAC7EH/5wK+bPfxK7du0tKs9ikNrn5s1tijbpbuvhw1/GqFE/8vVZ\nb7kmCyOGoDQbyeRWJJNJVFX9MySV8Wb1egPAQQgRwa5dewPKKzx3Nm9uw+7d50NSoO6DpGHNh1sV\nbw8kfe16SFXGtZCUq5dg1OVWwm8/+tqHYcZ0HLTSakXFi1i8+H1s27YcyeRzvjanUr/AjBlXI5H4\nc2X700E0AwcOzMPMmcvx7W9/HS+/vMylLppO34FvfvMSTJp0N+bPH4HDh7+IUaPasHZtHo2NtyMa\nnQmZBFznL5sMQwduhcwJ+I56304dohHBoUOfhBAfg52vDPg+YrHPoKrqOsi8bp/3XWfPDW1n5533\nAr7ylUYcPPgY8vks5Jh6n306Z5zuX1uFVacBWavGzs6LZvfncVRXP41vf/vv4Dj3wczFL6Or60F0\nddnJo21o1dVjkDniHoGkym31lK9TbvwGwJdBpNOUoKeOXV3fBtFVkNTQNki11iZPOTshaYPeZ+7P\n4H4uaOXVGhD9Dt3d+rM4JNV0A6Rq51wAZ+HYsXYcO7YBx47NhFRbXA1J6Ruh/q2GVFNsQDT6dcRi\n/xORyFchxFOQz6+nIOfmHtUHOl2GVvBcBUmN3QCp5HoEMn3ABfDbkc4t9wikWuiPVLn/BZlY/AzI\nsYaqny7/Wkga9N2qrpPUd9ZC0janwG07+wFcqf6/Xb1qIOdwq6qTnbZjC4S4Bd/5zkWYMOFOxGL1\nkJTKrQhOFyMVYh3nODZsuATt7Rdhw4ZLsH59DUaNegUyzYWmTb4Kqe7brfroLMh0BKMhqZTdqg8e\nglS6/BDkXHgQwBOQ1NaVkMnkz4OcF7sg8wzaartSjffAgYuGXR66snmGxbzAEToG46RGmIBDf79b\nTgTT9IqLrPQXxUUH8xSPL6fq6hv7XKewSGjY+0YBM2iXVO6USqGUlpLGyvS1V9RCRh2EmNkjiqIp\nYSaKWDhKaYuhGNU5b8RgFVVVTeuJvgANVFExhfyUvHDKpV/8wqZG+cemEAUyOBKXo5EjLwiMquoI\nUn+i2m4FUN1eN2W0ouJSSqXmUmXlForHV5PjTA2IgJjIlFuxNHzuSDqwLVJh0890pHMzyV3+djIi\nEjoy4aVsaftponh8s6JFakEHbz06KZG4WvXbFkqlZgVGbAtFeVOpOT6xH7+6ZZCt6EiOFsjQbZ1F\nRq0viIonX24qqLlHOl2rIvrBFFI9N9wKgfa9vJE1m35mRzZtoSW7jjry4xV8eZBisSnkOK3kOA8G\n2HI+gObsHquKCq2CadtoLZkIyRZV/warTfp6O9JuR9mIjChKEwFNFItNVuIiXnpfhqTKpT239bit\nJBnB8SoO60iptr+gcclTLPYA3XrrrbR48QM0evQtHuZBpyp7E5mIlo4e6vHx/kZkSdJkp5GcH1ow\nRNdZC7zoaNSFJCNumq6oRUVmkp9WnCVDqX3N+t5CMkJEuu/ayShkasrlIgK+QSNH6gi7/r6MdiYS\nLT02WldXp/piC8loZiP5xUryPaJe9nqhquoyMkI+dsS0joBPqc+03dv01Zese+TIT8u1RXHCFaYd\np7nfDB6UOUJXtoKKuhk7dAzGSYv+Kzr23YkqRaEy+N568dc6oFTQUpxJ8wMWnAagr+f2guh+ZtEd\nVKfCDkwhmL72nhVx/7j7zxHqBVYwrcy/qLYpd3bfetVBtVNhO5arCLiKotGHQ5U73WcUg86FFEeD\n9I//ayQXStMC+1inddCqkvX19b7zk4XswK8AGiQNnwmgtraHSqjX19e7bDMeX0Xx+FSKxVaQ46yk\n8eMX9thpNpu1KJz2+SN7Ia3PUGXIf+Yo/LxffX091dU1Ws6DPaaNFItNse5haFJeRUgzJsVt7LjV\nPd3j76bnesdWL9L1It5LxdP1bKdkcjpt3NjkSwNizlQWnpOmjrYTpxf9fuqf7Hd7k8Oee7ZDlyFJ\nc1ukxmw5ScXAa6jwXDTUbm0zjnM1xeNN5DgrKZmcTosX27ZpHHd32drZ8LZD20kHScqm12ZyVFGx\nkBYsWED33rvMsg1DNZRS+zOpqmqGmg/audBjpJUwNR3Udpxsamjwb0si0aLKtZ+F2mHSG1Kalm6X\nE/S80U5glxqnLo89ec+b2eeXtQKmHusVnvKzpBU2w21C1/2rFKS8GotNpSNHjngUT+VnqdQ81/Nr\n1KgpZJxNc29b8Vc/b/zUyjqSDr+dWqROtdFW79TOZwvJ5429WbGI3A6dnlt6Q8H7GyL7Ipmc7jon\n3BeU26FjyiWDwSgLgqmMbhpQX77b+31LSVgtVRn91Li1eOGF+7F162lYs+YIHn/8K0inx4aWUT5I\nyo8QNytKl6HpTZx4TmDi8WLbG4lEXHTXsPd37tyJw4evhVSu64ahId0BIRrhON+DEJoWaupdzFjp\nvk6nN1rJug2VacyYF5BOp3tolkePfg2SqnYHpPraeADXQohNPXS3J56Yh6amJuzbd6FVp4mQNBqb\ntrMdUuXts/BTzOxkzxfAcW7A+vWn4JVXFuGVV5IBScyPqjq1wiTd9oIK9oUf3ZAUn/8HSXXyUpNu\nwje+cREiEUl1nD+/HgsWVGLy5MOKpvlsQTuw6atdXbeDaB2MQhxgxiECqX7pTmhMFLX+3g1gPo4e\nrca8eXHMnfs4nnhiHrZtOx1PPvkxnH32KEQiMqm3EG4KphDanjZCJoQGDM3tKQBvQwidzP4TMMmZ\nt0ImcNZ2aXD22e9g7Ng0HnjgGRDphPLjIClmRxCNPoNI5EZI6tedkOp4h7F793/iwIG3epQkt2/f\njnQ6jVRqK9z0SA0/jXHmzIdx9ChBK6rKl1F8Nc+WfUgkLsbIkb8F8LeqPN3350Aq+90KScW7BpJy\n2gjgYbz++lTMny/btXZtHlu3VuPxx7+CfP4ourvzql3vQVLGmgFsQnX1TVi3bgF27dpr1RGQ9Drd\nl89B0mm1nbUhFrsP998/2TPHIwC+ACFuRjx+UM3d3ZDUVk0FPROS8rccwAwYhcI6yATnfmr3nj2P\nYdu20/HLX16Abdvuw2mn/QTAGfjNb6bjqad+h1hMqw/q+dml+kbXKwZJHxQwNrQFMkF4HYDvIjjB\n/V4QHcCaNf+NJUv249ixpTDJyy9ALPa/sHHjCPzqV+uxZcvXsGHDJVi8+AAc5yoYOuM5kJS+g5CU\nzscAfA1eezH1agXwACoq7sGRI204cuRMuCnk3aqMP4dM9v0CpFJjzFOOgFQB7bauWwupwvl1Va+r\nIdU4Z0JSMKPq+rtVeRfCUKPPglQW1RTCb0BSm3X5MUgVyAsgqYf6e5MA3KVsogmO808YNeoAKipu\n8PVBJHIDdu/eDccRkM/NLep1O+Sz1Pp2REDacRTyeTQNwGrE44fxgx+8j8cf/wr27t0VQEmfCGn7\n50ImpNf2V6/+fxvkHJkN+ZxfDPmMHgMgr8p4Q7XLpuVGIGm030Q8fj4qKkYiGt0LIW6GfL7cBCFe\nx+HD0zBp0t0F1xqDjnJ6h729wBE6BuOkRSlJtEtNuB2G/kT6wgVbClNAS81XZ1+XyWRCqVTeyEG5\n2xsGf444LcJwIy1duoLq6+tLFubwRpSCBCRsypsRTMiTSdhtdterq6eq/ukMUAQ10S47f14yOd3K\nGWfvvpbWf+4ITj3JqEGQkMWcEqPDeqfcjlxl1D0yLmpSEE2zN2XWYCqhFj3oPRpnonZeJUtzr0L0\nQ7/YS550cmYhNlEi0awiUZsVjdCrUKnr/CgBM12RU7Njb0e4bPu9gwxtyl23qqppPuXUxsZnVJQ6\nPE+mm8Y4i0wOwtsJuIRGj74xMHIqaZhtFCwgISlfI0ZcQjKi4P08T6nULEqnaxW1eCZJdcZgkQYz\nHt5IjZe+2EIy0jWNksmZVh3deSKBmbR06QrauLEpIJ+giZTIKKk9d92Rr3R6oetZGp4Pb6q6j6RH\nRqPnkl+opYP8iaXb6cMfnkhGYMNOcN9MMorkpaq6mQKG2rxFUXRnqOeSlxZrC/ZoCqqddJus+9tR\nR28Uz0vnayYp5uFVjW0iYLKajzLXoolK6zboqJQ3OponSZ/daI2bTc90Pzvj8c2Wim9wMu1Y7FGq\nrr6RHKeFHGdlKFXc/G6Yfgbyrt8N+ewPpjRWVDyifi+2UDy+IuAZRVRR8TWSypSa1TGPTLTSjizW\nUTR6kVI+1onf7eeNFtFpIhklvYIqKjaT4zxIqdQMymQ6qL29vV/HIIKAMkfoylZQUTdjh47BOGkx\nFJTLwXYM+3ruz74uHl9NicTV6jxaafTOcrU3vO1mgWjT/UoZqx07XgtUDQxLPB6sOulvn5vOFuyY\n2Y5xLpdTjoKXmtZJjlM8vdav6uh3fu2zgL3BpHE431qIaAdBL6RrKZWaRblcznOukKx6hDsf7np7\n+8okMY/HH7WUAe1E6zlKpWYpdcrwdBNy0VbYHoPSlzQ0NLtswE5Yb+Tp5VmW6uqptHHjZteGh99Z\n1edjtG3UkjtxsbGRMOVUk4Td3Q/azt39OY/MIlA7DVdQQ8PTBeaXlkC3N00WklT81FS8sPrajlQQ\nBc+knTCbL9pGV6n+XEJy0W8vsHPkOCuprq7OUiINnlP+ctsIaKLRo29RlOR2T730fZbQhg0bQmiu\nfrtpb29XdFpvnexng+2wPayUSC8nI2VvL+ZvJJNsPojCG3RmMUfAeqqouJyCNw10Ynt9PmsWGVn7\nRjJpGew62/PQTppuP1tWemykhfzOa53HiZLtFGIG+R3uvLKxuWTOzTaRnBv+ca6rq7PUPO2+N46z\nUajU5QdTf+vrmwLPUmq6dD6fp/b2dqqo0GkR3Bsdblqsd36bTYyzzppG5gyuvp/33ORV1Nj4jOc5\no2muNr23lmR6lGLb07/f3nI7dNFC0TsGg8EoFppuNHv2na5Ex+vW3epLvFnKdwcDfgqoVHrbt28U\ntm/fjkmTJrkobPp7HR1TMXt24eSiQdcBX0UqNRMNDV/DxImrS2pzkMJksaqTQUnWw8Zi/XozFsWO\nVXd3N2655TEcOPDnkCpvERABBw7MwC233IEdO1aHJJD/ESRtKxx+tUlNSTofjiMwZswLvqTa69ff\nihkzVuL1128A0RQIQUgmn0dj4/2Q1D6gpqZw/9fU1CCVqkNHx1RIKmAdpEreKuik4uPH/wWuv/7L\noWW4yxuHxx//Cs4//2PI5TT1x4HuL4mpOHLkBnzqU3dh//5qHD16WlFlB9e7Cm4q4TgAqxGLPYiR\nI1/DoUNbIGmu09XrLcRiX8e9996BVOrjuOCCn+PYMb+SZbE2p5Vtjd1935ccvrv7KNaunYXu7m7s\n3/+X6O7uRiTShbFjx2LixC29zI9xkOqLb6k2boek2R6HHmODnfDTS4F9+0ahpaUF9957JebNm44j\nR64DADjOA/jmN2/23H8ngEsgKaRtVlnX4OabL8Ho0R/FpEmTeq7R88skYl+nytgPSffqhhyfX8NQ\nwbz1tVUMz1Jt1ZBJ7I8ePQ9LlvwS+fxZVr9IG43Hj+Cee7qwYsUIHD2qqbZ7ANyNo0cvwIIFAqec\n8msIMRNEbvrcwYOTsX//fgCVvnId52dobLwN0Wglpk69C4cOzXVdK+36AObPH4No9G2kUnVYt24B\n/JDP2+7uNxCJVGPmzJnYvn07Xn/9Q5AUuTsgKYPnqe+fA0k73A4hnkIu9x1IWu02GErpWZDKh5q6\nTTAKr1fDftZXVW3BO+9Mgzu593XI5S4BMBXR6Odw/PhN6vt16l99j4/BqF4Ckjar1SDt54WmUd4M\nmbj8TyAplr9Rn9cA+B4k5bAW0kb2Qqo42tTyGlXGDPX5GgAXg+hTcJzpyOcvwvHjNwP4IioqDiOX\nOx9E4yHpnadD0pw/pcr4PGKx3+C003bgySe/A+AYIpG31b0/p/r+ZnXvywFsw9GjX7bqEwFwq6Jh\nXolIZASSya1Yu3Yubrzx+yCqhJuO+xi6us7DggUxfPvbX8a77xJyuT+DpH3eCZMw/iHkcjoJ/BrI\nZ+Neqy5XQog8/v3f/xUyL/fvAHwHcuzl58BISIrxb3HPPTVIpT6OdHpsz7Noz56/xIIFAkePGnuW\nlGibyi/buH//hfj7v98EolnwgwLeGyKU0zvs7QWO0DEYJz1KFSjpC33Rvr78kb5gpbu+RsfKGVXL\n5YKpdonE1b0e0O4tulhMgurexqoQhSZMFSybzSo6md4lnRE4nn61Sf+Ob1CbJV2tiWKxFZRMTqds\ntrO3bi7Qd0bQoS8RVrvNUixkHoUp4xVWBQynXOZyOVey8jAqoTvRvKblabtvo0Tiaqqvb7JETbwR\nvDm9Ui6L71c7ct3aq33672tHXmxqmzd6kPHsshulTUmxCqexuiNtt3v61CT5jsWaAyP3uVyOli5d\nSaeeepWlurqMZPSogYJzdmk6bFikxzt2QZTaYtVJvX1jnlMmOh4cvSQiam9v90Q/w6M37rqYiJ8Q\njQHqo3qMmgLapuvsjZzr54imIs4kf9LwNgJayXGmUn19k4pAagqle67F45Np3bp1nki5rleQSuQW\n8tuGrcyqI/KrSUbqtFrlCvLnCfRHFOPxVZRMzg7o73aqrr6UXnrppR7Ku4msenMXyiinFB9pcwlx\nuWngdp/ZVHcT6Y3HG2nx4sW0ZMkSWr9+E1VVTSF39KuFjMKrjPSZftbR1s0ELKWzzrqWqqqmkl8F\n1mtP9t+dJGm1M8n9HFtFwBUUizX7nil+sStS9fJTO+PxFSGRxOKo9mFAmSN0ZXfaCt6MHToGg1Fm\nlCNZuft8TPACNdihGFyHzu38aNpWLcXjqwqWNRBn78Lq53fo5A9/LLacMplMgbp1qB/2R9WPf4NP\nbbKUNgykkmpXVxfV19dTfX19n5TOTN06yUjb2/bhpVSaRaHjNFM6XUuNjc/47N68Z5z2TKYj8Nym\nSVoepn6Yp2RytqKHeumFl9PSpQ/1OI19mX9+qm/4WNmOXyLRSqnUDFq27JEeFchY7BES4sqAstxn\nucaP/6q1iNPUycKLZy991E2X9TpYwXam659ItAYswheSdDbsM2xtJBejXyKzEPbTZqVD6F1oGkqt\nVgnU42HUZFcG2Fw4HTWfz1Nj4zNqE0HaQDw+lZYte9ijlqsdlTaS9MFwipruy7B7BlOm3W1LJqer\nc25eJ1crGdar/plH0snznj3LUDy+gu6/f7ly5usC+oUIaFJ0Vv/GSzQ6k9zn/ILUebuouvpSWrx4\nMcXjOpG7TSdsImApjRx5CVVVTbdUNpdQRcUV5O2fdLqWNmzYoNIv2M8H9yak+3dNq8kWttlsttMa\nF2/ydXsDQZ/FfJSAz6nvP0QmvYPtiNWTUUfdQmZDxD7b+yABl9E119xI/vQlen7a/e/9u4vkfLEp\nquHz0m+vmu7tPSfXSfH4ZFVfN5UT+BItW/Zoyc9/DXboGAwGw4P+RvqI9NmvcGEE9xmu4h2EvjoW\nQW1yn+MJPmgehIE4exdWZ7kI0zLSwbvv/vp1eiJOcmGUSs1wOUylOO8D1eZy5U80i+smdf6lt2iJ\nPxrZe360HKVSMwIl8N278eHCBKNGXasWad5FdaNrZ99rq73NyeCzib3NO7c9jR+/kDZu3KwcVjvy\n8igJcQU5TgslEi2UTE6nhoZml3MYj68g9258eB1se8nn87RhQyOZSErh69zzP0jS/zUCJpPbKWi3\nFpbB4jDjxy+kpUuXBwj/yDJiseWBket83pvv0bzs1AL2/PI/w/w2oPNcptO15DgrKRZbFHDWK0uO\ns7JnYycsBUQi0UL19fW0dOmKgvMgONLXSnKR3kDSkZlM7jyIdr8vIunwzVD2cymFOXR1dXWBz/F0\nupaSSfuMmokMOU4LxWKPUiz2JYrFmpUjbYt4eM+72RGnOcpWvudiBCSTMp+iO8VL+O+LccSXW23r\n3WYbGlqoutp7zo/IpEzIW3WuJXfOQ2+dvIJA2tH2P6+i0YtUGZ0k8wLq67zROttB1WP5dTLzqHAb\n3b+l2gn35lfcqM7y5cidBsFEqfVZ876AHToGg8EYIBTKMWXnSys1GlHqdWFOQ1+dw746N31xlLPZ\nTorFPk9hqn1BdZX1KxwdKbVOAy8gU3z/Fyovm81SQ0OLy+EaP35hAB2oWDVO74K1uSfSGSRGIh3w\nIOGTvLVL39bzXjEqocU4vYUdOrP4r6urCxF3kd8zkUZznby2SeXMC07wvmTJEvLnnyq8o29TWSUt\n7EvkTsJdaOFo5y1zf9dxmiwFvaDv6Sj3A7R48eIeZ0ZGGYIST0vRmjA792+gmLbalN1gEZPi+imT\nySjbCt/Y8dtsnoAWEmKGUlEMSlIeLrqTSLTQ6NHTaOTIy6xnz8Pkz+1m/z9DJnK3g9yUR1knTWkP\neo5ns52UTM6kIKrmqFGXWeIeun12MvCgiFNQ/8qNmfb2dqtP9ffCk8yHC0kVt3nR1dVFp576RY99\n2f2VJXeeQ3tjyI4230/SwdZlhOULzJJ09HTkrJWAb5CcZxstBU7tuHuj17ZgUFAbzWaAm3HjZSno\nPtICNK9RmOrncBJFKVtBRd2MHToGgzGMUcyCvT9pC4q5rrc69MWp7Isj0tdIlHHOghMMD2Qai/62\nubi2DUyk02sfpY5zKYtu7303bmzyLDzlws19PilskeRuf7H9Hk65dC/+Da0u+N7mLGBpYyLVVb0b\nDjL6oFNf6D73Uj6NM5QjSdMLlzOX5yW1+qKdTsB9Fs1PdfO2VaflaHHNx0LOmdcRdZ8F9J8l854v\n1de6z2IVZwN6o2L8+IUBNFMZfe/q6vJE2Gw1Q/3d4HN4Xluy00TI6JWmpreQSaStI192tErTM7UN\n2hL2TSSEW73U25+GZq4jU1k1fu0k0xt4nZZOkk6jd4x1v9qOnZuF4Y5Y5lXb9Hk1/3j4VWj13GoM\nVLYNognHYo+SO0LvjfTZDp2tMqrr+DBJ522z9d4ikvRMr4OUUe/N85TRTh/5yPm0fv0mzznXDpKq\nlHr8bIqu9xnojiqn07XWppndDu3k15M5g1lLwSq07NAxGAzGsEU5zuT1B8U4DX1xKktpV3+cIVP/\n4naB+3u/crW5GAwWdVWjmHEOjogU3/embZ2USs1RzkzY+aTeyy2lj7xiMyaFgjs6IR0W7wJY1ice\nX07JZOm5Hf1naNoIWNiTl83vAOnyg87auXMg2naWzeozOPoafQ5HnkWLxaZQY+MzRGQzBArR8fxt\n3LixiZJJM3Y6cmRyP7b1OIENDc3W+OizVfXkOE2u8bGdWMdppljsS0XZgHcjSOaDDD/r1dj4TJGO\nrL9vg+04GyDko0U5ZqoF+m1knAlNB7TvbfolHm8MjfTq+7kdSJ3GYBn56YryFY0+RFVVN5BxNO0x\nttNbbFGvRRSLPaIokPZZyy2kU2YEOczt7e2B5/4cZyXddtvXlXMk7dBxru6xQ7/Na0d4I0kRGO0M\nZkjSImvJ5Ae0Nwo2q7rZUTQ99Tku8AAAIABJREFUf7w2/ZrqD0259NtLdfUUK2epnkcPkEkhsYXc\nkcFVBFxFFRWNAc8Vk+NRbrhcrr6vKZfNJKOKnyIZ9St+k6xYsEPHYDAYA4xynMnrKwYzCjQQdShG\nYCZMkVIKR8iEtX1VpOxrm4stq1RhloG0I+/iOZXS52uCRC9Ko9i6zyfZSnQzC7a/VNux79ne3h56\ntkuq+l1HwVE82W7tuJu/C0eX7TNfjrOSxo9f2As1VDt0/vbpc1/ec4SyD7UgR+FFofte9sL0uwHU\nQ3fEzqbUuqN9YRRVdxJxIWb15FJ027k3n119QSET//ywE48Ht/3ll19WTl/xfVvIhv0RyGYSwuv0\n2JGcWRQsThQcFfXeT+bL8yZeLyw21N7eTkuXrqRkcg7pnJjJ5CwaPXoOuSNi8pp4fLJyPLxCMdLh\ncpyrKR5/lISYRUI0kuO0eqJQ7o0As/nTmx2az2OxRUrtcapq7yPq/1epl+7zFgJW0IgRZ1t9qu3o\nFjL0S1sF80YyAjFh9tJE7oTuYdRTE910HCloExbF15s35lys3S87SAogaQfTzn/YRMnk7H5tELJD\nx2AwGCcxBipaVQr661TaCVzlAmOTS7Ey/L46StTiElsYCPTV2So26lcu8ZRC9Q8TaWhvbw9UtizV\nhtxJ4jcTsIxOPfVSSqXmhra/PNFdv921t7dTVdU0CjubqaNV7khl/53u4HNexbXPLbygoy/h88rf\ndzLaUl19qeeMaXgdjFMVfC8pEqOjKv4+dC/ogyKFGaqqusInshOe2iVfIPpGFIs9qs5p9S7wUYrd\n2ptDVVWXWJEdr7PWTPH4KkU5nu5pa3F21NDQXCC9hJvaWlGh588WnyOez+cpk8lYEU27n1Yop8RW\n6zTOSyz2gHUO03ymo1D2WLmjtH47DD7TnKeKilqKRjUl0nZebXq9caYqKmrJ7SQfIXkebq5VzxwB\ni8kkf+8k6awFReS1M+tVI9V03eDNpmIUqrPZrEdgqJOAiWTEW+yzeyspGr2IMpmOom0yCIPu0AF4\nEsDvAXQW+M73ARwE0AFgQoHv9avxDAaD8UHAUNM+y+FU2lTAQtS3ct6zWPTX2erNCRjothRSKuyv\ngI/3PkHOUW90xr7eu1C/mUVZ4bOZ/rOEcnGZSLT0KcIdXCd5bk1HVsLVW70Rt3AV3SCRD78iqU37\n9DooRI7TYp2bCqdGBqtHBvVhYfXRwuIp5hWPr1J0QW/btWKipuvZEUGZaiKd9kdN7bEJm4fec3VB\n9bIjf7lczqKotpLjrCyo0ukvvy2gv3REp4FiseU0evQ0SqVsZ8b/XAjrw0SiRW1o2JFEO8q6PHBM\nE4kWqqur6xECCY/A2ePvPZf5GslNlBvJCCj1frbWceyzua+RjHbZoiea4qjpqTqi2RkwVzRV8zUK\nViPNE3AXnXrqNN+8LOZ5bKizeiNjBmlRKeNklvd5PhQO3QUAJoQ5dAAuA/BP6v/nAmgvUFafG85g\nMBgfJAwl7ZNo8J3KwTqfNhiO40C2xaQ7sJP8Bt+jvzbUX+ptOSOgxZ7NNN+zF7utJHPnrehTPwTR\ngTOZjqLON7ptzZvcOdj2gvrOfd7Qm3JBvoTYVEAV1Hu2yi/woB0cE+UsHFHsvb2mfV1dXZ6ocZ6k\nc6KFNNwUyWj0YqqvbyqLaFMpcz5YCMZ9lquqapqL0ptO11J1tT5X5qWqthCwjJLJ6SqBeeEIbSaT\nCYyum/QItrCO18n3C7AIMUMl1jaMh1wuF3judMKERXTkyBEV6bPpkzPJRCu1kmRQNNJfXkPD0+Q4\nOr/bJWTOFdpzQUffplttyJE7Em8rWHaRFINx23Y8PpmOHDnimzv5fN7lrHudPe2US+qstnv9fNUC\nOYXVVvuCIaFcAjitgEP3GIAZ1t97AYwM+W6fG85gMMqHoXYWGCcGBtNOBsuhG4z7DNQ9wpUhTzzH\ntBCC7K7Ys5kmqlhYMr9U9JUO7HVQvef9SqmP7pe6urrAc2xCzKL29vYA5civETCtl7NVOurY1nMe\nM5mcWzDZeKH2Bp2FNTnRmkime1hGbql7E1GNxzeH2lixDpptRzrKWWy/h9tbEBWzg4DPkYk6zSQ/\n/S9H1dWXhkbVGxpaVL+1UkXFNygW+1KPraXTtbRs2cNWBC4stYVdz+CokhzXWSTPvs0iqeS5iVKp\nufTAA/9gJdHWZdYTYFMRdfuDk90HJbRvb2+naFSrY9p5Ae1ni87H2GDdR0fyvE4lkRFFeVh9toli\nsWbfvPSK+1RXX0pLly73pKHQAj6z6NRTp5KMQmqq5RYyKRaMfQJ5KpQapBgMR4fuHwGcZ/39UwCf\nDPlunxvOYDDKg4E+28Ng9AWDRbkcDCdloNrir7t2VprIcZrLHkUdDuc5bdhS6kLMICEaAs9myjNN\n5VOm64sDUej9ckRO43GthKgVOmspHl/lotvG42HOhftslXS+/Iv/dLqWNm5sCoxsFK6f3/k1tFGt\nPKkTQxeXR68YCmUwddX8zmWznSX1uzxDGkT9857tshNqy/xlblqwpl4+SEGCJ0akxD6ntZwqKiZS\nXV0j5XK5gHyDwUnqhZhF8fhmCsst6XaMdH0z9Dd/c5uygaB0Cg9SMJX0e64z0uPHL6SGhmYfzd5N\nZ3yagCsD7pMnk7xb10tHmrWt2EJBbQRcTx/5SOGUHX5xH7mxI3N92vRXvfnRQLHYA1RRcblq53SS\n0XDtXG5Rr1qqqpp2YlEuqcwO3X333dfzev755/vcEQwGo3QMtwUag2FjMGie/ZkDmhJln0cZzLaE\niU7oZLkDq6Q5NOc5vdDOQjzeRI6zklKpGeRVQzWiDqWnbwi+Z++bAKVulPXHqXNHj3TOrPYeURMi\nUk7AnICFs6m7XnS76YV6cR18XqwQvVQ7W0HnLv0J4DPKKbDPzwXPR7+a64wAkZMgcZnwPGvFwqSS\nCHPoghwe+zs6UqYjWv78f/X1TVaqBf35FgLaKBab4vm8sGhLOl1LdXV1HoEPu15hqRS0gEkQVdd2\ntNxU0tGjp1F9fVNPv4c50u6oeQcB3rQN2kHV5a+06qMdOmObMoq7ouA54nBxHyKjmEkhn3dSLPZ5\nMtE6v1podfWskuzp+eefd/lAw9Gh81Iu9zHlksEYnhgqChWDUSwGg+bZFyfFrfgoI0Cp1LwBW7SH\nlTcUGzLDhaJdSqSskNJjuR26UsfFpEt4kBznwT45yb3ZY7FnDv3t8+eKK2ZuGFrbg4HiHCYBvF3+\nagKuohEjvhcacQ3u21zBZOruCKaOqCzqiWAWg3AH1etI6Tx3Qc5Wjkz0znvmzIj1SIGjBykszUF1\n9RRyp5poU/ecTKeeOt0XPc1mswEOoHaalgXag3HotA3UknSq7qIRI/6OJEXzRiqUtqSQIy03Ymap\n679LwBIaOfJSqq6eRfH4oyQdJ5vqmSFgmqpHKwUp2/o3CdwCSOFzwI6qBjnp8jtybnWQoYl659CJ\nSbk8HcDukM8ut0RRPsOiKAzG8AU7dAyGRClOiv9clvnRt6Mig4HhFjEbTJTy/NJOU6lnwILQm8NW\nSr3yeTuhuaFvpVKl7faHKZD669Q77TT4vJih4xWycRMJLCTOUShdQo6SyenU3t7uU04tpOYaj6+y\nkqa750Emkwk9Y5jJZHrt27D8jkHnIA1d1Z+eoLp6KpmIWGHFUEmpXBn4nXh8s9XHeZLO3BwSopES\niRZKpWZQQ0NzwHi66xSPT1FiKH67Oeecr1q0W+3QNZF04vR9vekC3HYuHelVFOZIZ7OdlEzOoVis\nucdxz2Q6VNuCzir6KZH2GT1zrTuqmUhcTdlsZ4GNHa0Sq+8XNDZZMufoThJRFACNAH4LoAvAIQC3\nAFgAYL71nX8A8DqAXWF0S2KHjsEYcjDlksEoHe4zIO4fdcdpHvTNkOESMRtslLohVUjdrlS4BT2a\nyHGmUmPjMyXXq7/ORrF9EXx+KDwVgPu8WHjS8fBrTFvCHMj6+qaC6RK8Zfem5upNneAWjfHfR4hN\nvfZx2G+kN1VHkOCKVwgmk8lQPN5EwaqUXge8UwmSBEeCjGhKS1GbFGHiNO7IrhFE2bHjNWpsfEYp\nUuooXBC9NNzmNm7cTEERPL9Yj/nMHWWzI5DfDRjDHMVii2jJkiWUyXT0nKcNOpeo+yN4Y8dOgaDp\no94+taOZwfTWEy5tQVlvxg4dgzHk+CDv8DMYfcFwc+g+qOjrhlR/HeDezmSVUq/6+vpABwVoovr6\n+qLrVNq5vjYVIZvuiuR44XZAvAvvOSXQWk3Sbn/qiSCHxe3Q9UXN1U/79Kd1cJzecxH2lcVi21gu\nl/PQNXUESeddCxbzyWQ6rLxt/rYWiliGRYPDRHrCzgK3t7dbic2DqIp++qtOhTF69DQKimS502l4\nx2Slpz3SkYzFFnne185XM8XjTRblNjgi7E3fYm/suEWAdBlN5Dgmt+To0TMpGrUdbNvZbKJkcna/\n103s0DEYjH7jg7rDz2D0BcOJctkbTva5PRQbUqU6T4XqJSN0fYse2eiv8mZYmX41Rd1W93mh3iid\nQcnni61zqWqu/nKLy/cXBL8ISnEOnYbtWCYSrVRVdQVVV8+iRKKZHGelT0TEC6MQ2uyT/w/um9Id\nzkJ94C4/WCxEprdopXh8FTnO1eQ4rWrDaxkFJbyPxZqshPfuzyRlNDjvnqEUe+thO3HFCx+5lVI7\nfP2sVVCNI+4V7JFn+1KpGZTL5Xq1hd7ADh2DwWAwGIOMQlSl4YLhkJKk3BL9xdxjoFHsIrpYNUh/\nDjj5XqltGQjn1qR8KNzWYCGV3qMXxdS5VDXX4LqsJsBI6hcrfNSfc5dhMvlAQ6/RUW85YXZUyCnW\nkUHvdYWeC0Hz1V2+/+yappP6z062UJgaZC6XC623nSMwkWih0aOn0dKly2njxiYlHqQVL8m6VyGn\ns7hciY7TTLHY7VRdfSm1t++wnD07LYZO0dDcq22XCnboGAwGg8EYAhSiKg01hsP52HAxiRM752W5\n+9aoXDariJP/TFspdSvkRAZ93pvDUEhsxf6eu0+Kj14UU2c35bKwOEt4tLC0OgWdOQTqKZmcVpQz\nVooQTTH9EIYgp7ix8ZlAp62Q7RpHyn2Nt3ydX86up9/pzpNMb6AFf3SuuIWUTM7qOc8W5sxrWmRV\n1TSXcmsyOZeWLl0eQMss7HSGzSe/YMwWAlaREFeS47SGpMWQNqjTeJQL7NAxGAwGg8FwoS+CIQOb\nUmFghASGCuWOhuVyOaqvr6f6+vqy0LeCEBSZ8S780+la32K92LYWEoopR92LTRNS+DxfcXTJYAel\nhYSYoRKk974h0VuqCJ2iwBZS6W2zo9A5OPvMXlBqh1Rqhjq75pf2d5ymAKqjvCaTyYRG+zT81NTX\nSKYe2OS6D5D3nWcLa08YrX38+IUBmwyG+hnmdIaNkTulQ/C5wEJpMcoFdugYDAaDwWC4UKqkf7mp\nmf77lyep93BCuZzgwaDGBkdmvAtVHdlopMrKtoJUvPDy+5+8O6z+xUQKNYIVN/vr0JUWlS1OKKaF\nEonWohyGYu0k/MxhM8ViKxSF1q1cClxuiZ+4r3GcloI26aem6r7KUG8CJYX6v5DwlFb5tDcZ9Jm3\nUuaj/z7Bz6lCaTHKBXboGAwGg8FguFCKSMZAUDM/CA5dOTBY1NhgBz9L7uTNfa9HX8U5+lf/wuX7\nc+IV3ya/A9m7emIQgs/heaPVxSkzFmsnhYVMNBXS6xzb4jzF20IwNXWFsqu+21QxSsLl2FDJ5XJU\nXT2FihFVsdNi9Bax7AvK7dBFwGAwGAwG44RGJBLBunULMGHCnais3ILKyi1Ip+/AunULEImYn/qd\nO3fiwIHJgOvnP4IDBy7Gzp07+3z/mpoapFJbAXTrdwA8b/0NAN1IpX6BmpqaPt/nRMdA9X8fagJg\nYOvR3d2N7du3Y/v27eju7u79gvCSAGwHsB1E4eVEo1E0Nd3V6xwIwq5de3HkyHsQ4mYAzQD+GUIc\nK7mmNTXjsGPHatTXfwmp1HxUVrbBcR6CEOfB3dfC177u7jd6+qkUO3HPPe+4RgBcBOBvPWVNBPCj\nkGvC7+Wu1zgAqwB8XJUTgUxTfSeALQCakUzOK6r/a2pqMGbMIQQ9M8aMeQE1NTWIRCKYOHEiJk6c\n2Gt5Qdi5cw8mTbob7747GcBGdZ8aAFt990ylfmHdy8GkSXfjoovexkUXvY2JE+/Ezp17Sr7/QIMd\nOgaDwWAwTgLU1IzD9u2rsG3b6di27XTs2LEaNTXjBuXetkOZSLTCcR5CVdWbSKUWlLy4ZvQffgcb\nANJIJJo875WzfOOw79y5BxMn3tnnRbApfzekg/A2gLcAbAYQK3Bd6XOgu7sbs2evwYEDG0C0AcBo\nAJcgHm8NbV8hRCIR3HjjNOzduxbbtp2Bxx//GBIJu862E7FHte8tdHVVYd68jSU7C/bcc5yfATju\n+cZZAKLeqxCLTUQqNT/kmqLvDuAGCPFDyPZoJ68aqdTT+NWv1hT1DIpEIli//lakUsapFqIRqdQC\nrF9/a7+fGXqMOzpW4dixOwEsAzAfQmxGPH4mHOdaOE6r7zllX/f++9fg/fevQUfHKsyevaafmxQD\ngHKG+3p7gSmXDAaDwWAMGQaa8mfyaLX0nD0pRqzgg4LBVCMtpIaYSLT0WZ6/UPm9KSuW0sZstnNQ\nxCnC6J3lOkcVrNrZRPH41NAxKCTzX+gMnz+dAFEhkY9cLhdyTTGUS/PdVGoWpdO1ZemrgVAS7i0V\nRhilciCpxeAzdAwGg8FgMPqKgUrOPRxSJ5wIGMzk6IXSFjQ0tPR7ER5UfrkWwbKcvolslIJC9bXP\nUfVXCMer2llVNdUjTOJuX1/tpHBag+CySrlXIUe+lL4azHySfbXJE8mhE7LMwYEQggbzfgwGg8Fg\nMPzo7u7uOR+jz6f0F9u3b8dFF72N99+/xvV+ZeUWbNt2OiZOnNjve5wsGIj+Hy71KJcdDJY9dXd3\nY+LEO9HRsQrmJFI3Jky4E9u3rypLn3R3d+OTn7wDu3attu7xKoR4HUTXu75rt6+v4xN0XW9llXIv\n73cBlFTPnTv3KJrrZABAKrUV69YtGDCKeF/HeCBtQwgBIhK9f7PI8tihYzAYDAaDUSq8i7qdO3ey\nQ8co2yJ4MBwtDeNgXAwASCa3Yv36W8vmYAQ7p90Q4mbIc3sD276BRKnOmX9cpShMKvUw9uxpQDTq\nPe9X7nqWNsYDZRvs0DEYDAaDwRhSBC3innhiHubOfXxQFuCM4Y1yLYIH2tGyMZBR07BoYzy+Gqed\nthvvvHMZgIFt30CgL063uy/2AFgDqbJ5HKnUc2hqumtAI3Xlinj2F+zQMRgMBoPBGDIUWsRpp24w\nFuCM4Y1yLYKHCz21Pyg0Z1599RHs2rULQHj7BqIPylFmX2ix5pqpkAqfA78BNBxtqNwO3cDENRkM\nBoPBYJyUKJQjCziG7dtXWYun1cNi8cQYfOi8YcOlnKGETi0we/adrs2OdetuRTQaLdg+fzS8rt/n\nzQaizGIhU1LUoaOjCoXy35VrzIeyrYMJjtAxGAwGg8EoGix+wmD0DaVGigbiHGE5y+xrWTt37sF1\n1y3FgQPXAJju+qy/zxG7j9PpNCZNuntY0sDLHaHjbTMGg8FgMBhFo7ek0gwGIxg62jhx4sSinIlC\n0XDttJSKcpZpJzWvrNziS8wdhpqacdizpwGp1HMo53PEm9B+3LiZ2LfvIpSz/4YrmHLJYDAYDAaj\naBSijzG9ksH4YKGmZlyfaNbRaBRNTXeV7TnS3d2N2bPXuKJxBw5UQYjXSy7rRARTLhkMBoPBYJSM\n4Sg0wGCcTBjulMtyoFzPkRMtNQSLojAYDAaDwRhynAxiFQzGcMZARMOHW4R9YJ8jEcRiE3HaafNd\nqSFORjYBR+gYDAaDwWAwGIwhQm9RquGatmA4ob+pIQYbnIeOwWAwGAwGg8E4CeCX1d96UsrqDwYG\nMxF9f8EOHYPBYDAYDAaDcYJjuJ1nOxlwokQe2aFjMBgMBoPBYDBOcHBOxw8uWBSFwWAwGIxhjhNl\nl5jBYDAYJz74F4bBYDAYjDLCm9x24sQ7sXPnnqGuFoPBGGaoqalBKrUV5UyuzfhggimXDAaDwWCU\nCXwmhsFglIITSciDUT7wGToGg8FgMIYp+EwMg8EoFUzR/uCBz9AxGAwGg8FgMBgnCQY2uTbjg4Ci\ntgCEEF8UQuwTQhwQQvyPgM8/IoT4kRCiQwixWwhxc9lrymAMMLZu3TrUVWAwAsG2eeLgg3Ymhm2T\nMZzB9sn4oKBXh04IEQHwDwC+AGAcgOuFEGd7vlYLoIOIJgD4LICHhRAc/WOcUOAHP2O4gm3zxEEk\nEsG6dQswYcKdqKzcgsrKLUin78C6dQtOShoV2yZjOIPtk/FBQTFO16cBHCSitwFACNEEYAqAfdZ3\n3gVwjvr/nwL4AxEdL2dFGQwGg8E4EVBTMw7bt6+yzsSsPimdOQaDwWAMDxTj0H0cwGHr73cgnTwb\njwP4mRDitwA+BGBGearHYDAYDMaJBz4Tw2AwGIzBQq8ql0KILwP4AhHNV3/PBPBpIrrd+s7fA/hr\nIrpTCHEWgJ8AGE9E/+UpiyUuGQwGg8FgMBgMxgcag61y+RsA1dbfo9R7Ns4H8AAAENEbQohfAzgb\nQNb+UjkrzmAwGAwGg8FgMBgfdBRD6n8VwGghxGlCiBiA6wA86/nOXgCXAIAQYiSAFIA3y1lRBoPB\nYDAYDAaDwWC40WuEjojyQohaAM9BOoBPEtFeIcQC+TGtBbAcwHohxC4AAsA3iejfBrLiDAaDwWAw\nGAwGg/FBR69n6BgMBoPBYDAYDAaDMTwxaDrKvSUnZzDKDSHEk0KI3wshOq33/lII8ZwQYr8Q4sdC\niD+3PrtHCHFQCLFXCPF56/1PCiE6le2uGux2ME4+CCFGCSF+LoTYI4TYLYS4Xb3P9skYUggh4kKI\nV4QQO5V9fle9z7bJGBYQQkSEEDuEEM+qv9k2GcMCQoi3hBC71PMzo94bFPscFIeuyOTkDEa5sR7S\n5mx8C8BPiWgMgJ8DuAcAhBCfADAdwFgAlwH430IILeLzAwBziCgFICWE8JbJYJSK4wDuJqJxAP4W\nwEL1TGT7ZAwpiKgLwGeJqAbAeACfE0KcD7ZNxvDBHQB+Zf3NtskYLugGMJmIaohIp3gbFPscrAhd\nT3JyIsoB0MnJGYwBAxG9CODfPW9PAVCn/l8HYKr6/1UAmojoOBG9BeAggE8LIU4F8KdE9Kr63kbr\nGgajTyCid4moQ/3/vyCFpUaB7ZMxDEBE76v/xiHXCf8Otk3GMIAQYhSAywE8Yb3NtskYLhDw+1aD\nYp+D5dAFJSf/+CDdm8GwcQoR/R6Qi2oAp6j3vTb6G/XexyHtVYNtl1FWCCFOBzABQDuAkWyfjKGG\norTtBPAugK1E9CuwbTKGBx4F8A0AtgAE2yZjuIAA/EQI8aoQYq56b1Dss5g8dAzGyQxWBWIMGYQQ\nHwLQBuAOIvovIYTXHtk+GYMOIuoGUCOE+DMAPxZCTIbfFtk2GYMKIcQVAH5PRB3KJsPAtskYKpxP\nRL8TQvw1gOeEEPsxSM/OwYrQFZOcnMEYDPxeyFyJUGHtf1Hv/wZAlfU9baNh7zMY/YIQIgrpzNUT\n0TPqbbZPxrABEf0ngB8C+BTYNhlDj/MBXCWEeBPAZsjznfUA3mXbZAwHENHv1L//D8D/gTxyNijP\nzsFy6IpJTs5gDASEemk8C+Bm9f+bADxjvX+dECImhDgDwGgAGRUef08I8Wl1WPUr1jUMRn+wDsCv\niGi19R7bJ2NIIYT4K63CJoRIALgUwE6wbTKGGET0bSKqJqIzIdeRPyeiWQD+EWybjCGGEKJSsW4g\nhPgTAJ8HsBuD9OwcFMplWHLywbg344MLIUQjgMkAPiKEOATgPgArALQKIWYDeBtSYQhE9CshRAuk\nclYOwFfJJGlcCGADAAfAD4nonwezHYyTD0o18EYAu9VZJQLwbQArAbSwfTKGEB8FUKcWEhHICPLP\nlJ2ybTKGI1aAbZMx9BgJ4Gl1dCIKYBMRPSeEyGIQ7JMTizMYDAaDwWAwGAzGCYpBSyzOYDAYDAaD\nwWAwGIzygh06BoPBYDAYDAaDwThBwQ4dg8FgMBgMBoPBYJygYIeOwWAwGAwGg8FgME5QsEPHYDAY\nDAaDwWAwGCco2KFjMBgMBoPBYDAYjBMU7NAxGAwGY1hDCPFH9e9pQojry1z2PZ6/Xyxn+QwGg8Fg\nDDTYoWMwGAzGcIdOmHoGgBtKuVAIMaKXr3zbdSOiC0opn8FgMBiMoQY7dAwGg8E4UbAcwAVCiB1C\niDuEEBEhxINCiFeEEB1CiHkAIIS4WAixTQjxDIA96r2nhRCvCiF2CyHmqveWA0io8urVe3/UNxNC\nfE99f5cQYrpV9vNCiFYhxF59HYPBYDAYQ4XoUFeAwWAwGIwi8S0AXyOiqwBAOXD/QUTnCiFiAH4p\nhHhOfbcGwDgiOqT+voWI/kMI4QB4VQixhYjuEUIsJKJPWvcgVfaXAYwnonOEEKeoa36hvjMBwCcA\nvKvueR4RvTSQDWcwGAwGIwwcoWMwGAzGiYrPA/iKEGIngFcAfBhAUn2WsZw5ALhTCNEBoB3AKOt7\nYTgfwGYAIKJ/AbAVwCSr7N8REQHoAHB6/5vCYDAYDEbfwBE6BoPBYJyoEAAWEdFPXG8KcTGA//b8\n/TkA5xJRlxDieQCOVUax99Losv6fB/+WMhgMBmMIwRE6BoPBYAx3aGfqjwD+1Hr/xwC+KoSIAoAQ\nIimEqAy4/s8B/Lty5s4G8Bnrs2P6es+9XgAwQ53T+2sAFwLIlKEtDAaDwWCUFbyryGAwGIzhDq1y\n2QmgW1EsNxDRaiHE6QB2CCEEgH8BMDXg+n8GcKsQYg+A/QBetj5bC6BTCLGd6P+zd+fxUVX3/8ff\nJyEJBAg7IgiJAmEVEoJmLgWjAAAgAElEQVS4J6itexWlUNzA0sVagS+2v7b024qI2mpbq4Kt1VYQ\nVFQoCtjq1z0JCCKEBDUhhC2BACrITsg65/fHTUKWyQJM5s4kr+fjMY9k7r25czJGzTufc87H3lXx\nWtbaN40xF0naKMkj6VfW2m+MMYPqGBsAAK4wzhIAAAAAAECwYcolAAAAAAQpAh0AAAAABCkCHQAA\nAAAEKQIdAAAAAAQpAh0AAAAABCkCHQAAAAAEKQIdAAAAAAQpAh0AAAAABCkCHQAAAAAEKQIdAAAA\nAAQpAh0AAAAABCkCHQAAAAAEKQIdAAAAAAQpAh0AAAAABCkCHQAAAAAEKQIdAAAAAAQpAh0AAAAA\nBCkCHQAAAAAEKQIdACBgGGOSjTEHjDFhbo8FAIBgQKADAAQEY0y0pFGSvpF0kx9fN9RfrwUAgK8R\n6AAAgWKipPclLZR0d8VBY0xrY8wTxphcY8xBY0yqMSai/NxlxphPyo/nGWMmlh//2Bgzuco9Jhlj\nVlZ57jHG/NwYkyMpp/zYU8aYncaYw8aYdcaYy6pcH2KM+V9jzFZjzJHy872MMc8YY/5S9Zswxiw3\nxvxPk7xDAADUQKADAASKiZJel7RE0jXGmG7lx5+QFC/pIkmdJf1akscY00fS25KeltRVUpykjHru\nb2s8v1nSBZIGlz//TNIwSZ0kLZK0xBgTXn7ul5J+IOlaa22UpMmSCiQtkDSh4obGmC6SrpL0yql8\n4wAAnC4CHQDAdeXVsF6SVlhrt0jKlHS7McZI+qGkadbar6zjU2ttiaTbJb1vrV1srS2z1h601n5+\nCi/7B2vtYWttkSRZaxdZaw9Zaz3W2iclRUgaUH7tjyT9zlq7tfzaL8pfb52kw8aYq8qvmyAp2Vq7\n/8zeEQAAGodABwAIBBMlvWetPVb+fImkSXIqb60lbffyNb0lbTuD18yv+sQY8/+MMVnl0zcPSooq\nf/2K1/I2Bkl6SdKd5Z/fWf4cAAC/aOX2AAAALZsxprWk8ZJCjDF7yw9HSOog6WxJJyT1lfRFjS/d\nJWcTFW+OS4qs8ryHl2sqp2CWVwh/JekKa21W+bEDkkyV1+orKcvLfV6S9IUxZpikgZKW1TEmAAB8\njgodAMBtt0gqlTRI0vDyx0BJK+VU7uZJetIYc3b55iQXlbc1eEXSVcaY7xtjQo0xnY0xw8vvmSHp\nVmNMG2NMPzlTJuvTXlKJpG+NMeHGmJnlxyr8S9LD5feSMeZ8Y0wnSbLW7paUJifYLa2YwgkAgD8Q\n6AAAbpsoaZ61dre19puKh6S/yVknN0NOdW6dpG8lPSYpxFq7S9L1kv6fpAOS0uVsaiJJT8oJaF9J\nmi/p5RqvWXODlHfLHzmSdsjZ8GRXlfN/lbRY0nvGmMNyAl6bKucXSBoqZ4dOAAD8xlhb8/9pXi4y\n5lpJT8kJgC9Yax/3cs1oOf8DDZO0z1p7hW+HCgBAYCqfsvmytTbG7bEAAFqWBgOdMSZEzl8sr5K0\nR85fSCdYa7OrXNNB0mpJV1trdxtjurLDFwCgJSif/vmqpHRr7aNujwcA0LI0ZsrlKElbrLV55dtE\nvyand09Vt8tZN7BbkghzAICWwBgzUNJBSWfJ6YcHAIBfNWaXy16qvo4gX7V3FYuVFGaM+VhSO0lz\nrLVs2wwAaNbKZ6u0c3scAICWy1dtC1pJGiHpSkltJa0xxqypaMBawRjT8II9AAAAAGjGrLWm4asa\npzGBbrekPlWen1N+rKp8SfuttYWSCo0xqXK2nd5a4zo1ZhMWwA2zZs3SrFmz3B4GUAs/mwhU/Gwi\nkPHzWT9rpf37pW3bnMfWrdU/HjmSpqKiPFl7a7WvM2apIiJi1KpVgnr2VL2Ps8+WIiPrGEALZozP\nspykxgW6dZL6GWOiJe2VNEHSbTWuWS5prjEmVE4z2AvlbPEMAAAAwAUej7R798mQVjWwbdsmhYZK\n/fpJffs6jyuvlH7yE+fzs86K18iRC5SRMUYnt93waPjwFK1ff4uOH5f27Kn+yMuT1qypfqxNG+9h\nr1evk5/36CGFh7v5TgW3BgOdtbbMGDNF0ns62bZgkzHmHue0fd5am22MeVfS55LKJD1vrc1q0pED\nAAAALVxxsZSb6z2w7dghde58MrD16yeNHXvyeefO9d05RPPm3aPJk6crJydJktS/f7LmzfuZQkND\nFBUlRUVJAwfWfQdrpYMHnVBZNeRlZ0sffXTy+ddfSx071l/t69lT6t5dauWrBWPNSKP60PnsxYyx\nTLlEoEpOTtbo0aPdHgZQCz+bCFT8bCKQNaefz+PHvQe2rVulvXudaldFYKsa3s4778ynPHo8HqWn\np0uS4uPjFRLSmE3yT01ZmTP9s2roqxkC9+yRvv1W6tatenXP26NLF6kJhukzxhifrqEj0AEAAAAu\nslY6cMB7YNu2TTp8WDr33NqBrW9fKTpaCgtz+zvwj5ISp5pXM+jVDIHHjjnTOOsKfBWBsEMHycfL\n2RqFQAcAAAAEGY/HqaZ5C2xby7cR9BbY+vVzNhcJ5IpToCksdN7ruoJfxaOkpOFpnj17Su181Jym\noto5cuRIAh0AAHCPP6ZgtSS8n81HSYmzMYi3StuOHU5FqGZgq/i8c2d3qkUt2bFjTvDzNr2zatUv\nLKzuDV2q7ujZunXdr5WenqnJk59TTs5oFRSMJdABAAB3VP2lRJJiY5M1b949io8f4uq4ghXvZ/Ap\nKJC2b/deadu92/nFvq71bL6q9MB/rHWmvNY1vbPisXev1L699wpfjx4e/fa307Vly1Ny9phkyiUA\nAHCBx+NRQsJ0ZWRU/FIiSR7FxU1XWtpTVJZOEe9n0/BFxfPgwbq3+j9wQIqJ8T49MiaG7fdbKo/H\n2bTFW5UvKytNq1blyeOp6Onn20DHxp8AAKBR0tPTyytJVX9BDtHmzUl69910DRuW4NLIgtPnn6dr\n8+bR8vZ+rlyZrpEjExQa6mzTHhrKdLzGqF3xXOC14mmtU1Gpq6l2aWn1wHbppdLEic6xXr1Yz4ba\nQkKcHTi7dZOGD69+Li1NSkx0qrtNgUAHAAAazdtEmxMnnF92qUycmuJi572r6cQJ6YYbnPe6tNTZ\n0r2szAl1FQGvIuRVfF7z+eme89V9/HEuJKR6yPV4PJo8+blqFc+MjDH6/ven6xe/eEo7doRUBrbt\n253pj1UrbDfccDLEde1KgIbvxMfHKza2ZpN23yHQAQCAehUVSf/5j/Tii/EqKlogqeovJR7FxaUo\nLe0WqhanyOOJV0JCzV/yvL+f1jqhriLglZbW/tyf5woL3X390lJnilv1Cma6jh8frZoVz9zcJH3w\nQbouuihBF198MsS1b++3f9Ro4UJCqjdp93WljjV0AACgFmuldeukBQuk11+Xhg2TJk2S+vXL1JQp\nzyknJ0mS1L9/subP/xmbeJymk1MEeT9PVdWQW1oqpaWl6brr8nTixK3VrouMXKrU1BglJDAlGO6i\nbQEAAGhy+fnSSy9JCxc6vyRPmiTddZfTvLgC2+z7Fu+nb7DJDIIFjcUBAIBPHT8uvfmmU41LS5PG\njXOC3MUXs44IwYWKJ4IBgQ4AAJwxj0daudIJcW++6YS3SZOkm26S2rRxe3TA6aPiiUBHoAMAAKdt\n2zZnOuXChc4uf5MmSXfc4TRDBgA0PV8HOna5BACgmTt8WFqyxKnGbd4s3XabtHSpFB/PlEoACHZU\n6AAAaIbKyqQPPnBC3NtvS1de6VTjrruOfnEA4CamXAIAgDplZjoh7pVXpJ49nRB3221Sly5ujwwA\nIDHlEgAA1LB/v/Tqq06Q27vXaTPw/vvS4MFujwwA0NSo0AEAEISKi52plAsWSB9/LN1wg1ONu+oq\nKTTU7dEBAOrClEsAAFooa6UNG5wQ99pr0sCBTogbN06KinJ7dACAxmDKJQAALczevdLLLztBrqBA\nmjhR+vRT6bzz3B4ZAMBtVOgAAAhAJ05Iy5c7Ie7TT6Vbb3WqcZddJtEnGQCCFxU6AACaKWul1aud\nEPfvf0sjRzohbulSKTLS7dEBAAIRgQ4AAJfl5kovvSQtXCiFhTkh7osvpF693B4ZACDQEegAAHDB\n0aNO5W3BAunLL6Uf/EBatMipyhmfTcQBADR3rKEDAMBPysqcFgMLF0orVkhJSU417oYbpIgIt0cH\nAPAH2hYAABBkNm92KnEvvSR16+aEuNtuk7p3d3tkAAB/Y1MUAACCwMGDTq+4BQukvDzpjjucRuDn\nn+/2yAAAzQkVOgAAfKSkRHr3XSfEvf++dM01TjXu6qulVvwJFQAgplwCABBwNm50QtyiRU6z70mT\npPHjpU6d3B4ZACDQMOUSAIAA8PXXToBbsMCZXnnXXVJqqhQb6/bIAAAtCRU6AAAaqahIeustJ8St\nWiXdfLNTjUtKkkJC3B4dACAYUKEDAMCPrJXWrnVC3JIl0vDhToh79VWpXTu3RwcAaOkIdAAAeLFr\nl9NmYOFCyeNxQtyGDVKfPm6PDACAkwh0AIAWwePxKD09XZIUHx+vEC9zJI8fl954w6nGpadL48ZJ\n8+dLF10kGZ9NjgEAwHdYQwcAaPbS0zM1efJzyskZLUmKjU3WvHn3KD5+iDweZzOTBQukZcukSy5x\nqnE33SS1bu3qsAEAzRBtCwAErMZUQNB4vJ++4fF4lJAwXRkZT0mqeA89GjRousaOfUovvxyi9u2d\nEHfHHVKPHm6OFgDQ3Pk60PHbAQCfSE/PVELCdCUm5ikxMU8JCdOVnp7p9rCCFu+n72zYkF5emav6\nv7wQbdqUpK1b0/XGG04fuV/+kjAHAAg+VOgAnLG6KiBxcdOVlvYUlaVTFMjvZ1mZVFwslZRU/+jt\nWH3nfHF9Y+9RXJwmKU/SrdW+l8jIpUpNjVFCQoIr7yUAoGWibQGAgJOe7r0Ckp2dpPnz0zVgAL8w\nn4rNm9OVnT1a3ipKTz6Zrt69E1wLS9ZK4eEnH2Fh3j+ezrHwcKcNgK/vGxoarwsuWKCMjDGqGpBj\nY1MUH3+L3//5NgdMBwaAwEGgA3DaDhyQ1q+Xli93Gi7XVFwszZ1Lr65TdeyY897VVFLiNLXu1q3h\nINO2bf2B53QDUmio/9+PMxeiefPu0eTJ05WTkyRJ6t8/WfPm/YwgchrSN6Zr8szJymmfI0mKPRqr\nebPnKX54vMsjA4CWiSmXABrl8GGnB9f69Scf+/ZJI0ZICQkeLV06XXl5gTdFMBgF8pTLYEZV6cx5\nPB4l3JKgjLiMqj+aisuIU9qbabynCAj8u45Axy6XAJrc8eNOD67166V165yP+flSXJw0cuTJR2zs\nyYrNyW3hT1ZA5s//meLjh7j4nQQv3k8EorS0NCU+maiC/gXVjkduiVTq/amsR4TrqCAjGBDoAPjU\niRPODn9VK2/bt0tDhzqh7YILnI+DBkmtGpikzV9FfYv3E4GksLRQC99ZqCmLpqhkYEm1c62yW2nh\n3Qs14eoJMnRgh0uoICNYEOgAnLbiYumLL6qHt82bpYEDq1fehg511kwBaLlOlJzQp/mfKjk3WSl5\nKVq/Z70Gdx2s3Ndyte/yfdV+Ye62spsivxepiLAIjR88XuOGjNP53c8n3MGv6qogt85prfmT5uvC\nCy5U+4j2ah/eXhGtIlwaJcAulwAaqbRUyso6GdzWrZMyM6W+fU8Gtx//WBo+XGrd2u3RAnDb8eLj\nWpO/Rim5KUrOS1b63nQNO2uYkqKTNOOyGbq096VqH9Fe6RdWn9LW/0h/zX9yvuKGxWn9nvVanLlY\nN716k1q3aq1xg8dp/JDxGtp9KOEOTergiYN6e8vbKiqrvUNXcVmxHvj4AZVuKtXRoqM6UnREkirD\nXZ0f6ztX42NoSFDuGNUozBYJfFTogGagrMyptFWtvG3cKPXuXX3aZFycs/shABwrPqZPdn6ilLwU\nJecm6/OvP1dcjziNjhmtpOgkXdL7ErUN9/4fjIZ+wbPWat2edVqSuUSLsxYrMiyyMtwN6TaEcAef\n2HV4l5ZvXq5l2cv02e7PdEX0Fcp4MUM7L97Z4JTLotIiHS0+qqNFRxv3sZ5zx4qPKSI0Qu0j2isq\nIuqMw2FkWGTA/DvCmsSmwZRLoIXzeKRt26qHt/R0qXv36tMmR4yQoqLcHi2AQHGk6IhW7VyllNwU\npeSl6MtvvlRCzwQlRSdpdMxoXXTORYoMi/T561pr9dnuz7Qka4kWZy5Wu/B2Gj9kvMYNHqch3dnk\nB41nrVXmvkwty16mZdnLlHsoVzfG3qgxA8fou+d9V23D29YKIP2P9Nf8h+c3aQCx1qqgpMBr2DtS\ndOSUQ2JRWZHahbfzWfXwdKeXsiax6RDogBbEWikvr/q0ybQ0qUOH6uEtIUHq3Nnt0QIIJIcKD2ll\n3kql5DkBbtO+TRrVa5SSopOUFJOkC3tdqDZhbfw6Jmut1u5eqyWZS7Qka4naR7SvrNwN7jbYr2NB\ncCjzlGlN/prKEFfqKdWYgWM0ZuAYXdbnMrUKqb16KNinCJZ6SnWs+NgpVw+PFB3xet7I1Bv6osKj\nvB7fu3mvfrH4FyqMLaw2Pna1PXMEOqCZslbas+dkm4CKR3j4ySmTFeGte3e3Rwsg0Bw4cUAr81ZW\nbmKy5cAWXdjrwsoplKN6jQqojSA81qO1+Wu1JMsJdx0iOlSGu0HdBrk9PLiosLRQH2z/QMuyl+mt\nnLd0druzK0Pc8LOGB8x0xGBgrVVRWVHjw2GVkLhn8x5t2LRBnkGe6jfNktp1b6cO53ZQ2/C2ahvW\ntvJju/B2J4/VdbyeY2GhYe68UX5S8ceGkSNHEuiA5uDrr6sHt/XrnY1Mqoa3kSOlnj3dHimAQLS/\nYL9S81IrA9yOgzt0ce+LNTp6tJJikjSy50iFhwbHdrUe69Gn+Z9WVu46telUGe4Gdh3o9vDgBxWb\nmizbvEzvb3tfcT3iNGbgGN084Gad2+lct4fXItU15XJYxjB9uOhDnSg9oeMlx3W8+LiOFR+r/Lzq\nx2PFx04eq+t4lY8hJsRr0GsbXh4AGxEUq11b5Zi3aq4/VZ0OXPBKAYEOCDbffutMlaw6dfLYsZOh\nrSLE9e4t8YdHAN58c/ybyvVvybnJ2nVkly7tfWnlGrgRZ49oFn/d9liP1uxaU1m569KmS2W4G9B1\ngNvDgw/lH8nX8uzlWrZ5mdbmr9UV516hMQPG6HsDvqeukV3dHh5Ue1OUplyTWFFN9Bb0vAXGWsca\nCJdhIWGNC3/1hEJvx9qGtW1wl9Na4XiWCHRAIDt8uHp4W79e2r/fmSpZtfJ23nmENwB123t0r7P+\nrTzE7T22V5f1ucxZAxedpPiz413/i3NT81iPVu9arSWZS/TvTf9W18iuleEutkus28PDKbLWKmtf\nlrMebvMybT+43dnUZMAYXd336jp3VYW7gn1NouT87BWWFjZcRfQWGBsIkgUlBQoPDfca9CoqiCfy\nTuid9e+odGCpM6BZLgQ6Y8y1kp6SkylfsNY+XuN8kqTlkraXH3rDWvuIl/sQ6BBwzuQ/VMeOOTtM\nVg1vu3c77QGqhrfYWCkI//sHwI/yj+RXhreUvBTtO75PidGJlZuYDD9reLPuddWQinC3OHOx/p31\nb3Vr262yiTnhLnCVecr0af6nlSGuuKxYYwY46+Euj7682f9RAs1fRVisb9pp5sZM/em/f1LxgGLn\ni2b5OdAZY0Ik5Ui6StIeSeskTbDWZle5JknSL621NzVwLwIdAkp6eqYmT35OOTmjJUmxscmaN+8e\nxcfX3kr7xAmnt1vVaZM7dkjnn1996uTAgVIr/v8EoAE7D+90mniXr4E7VHhIidGJlZuYnH/W+Qox\n/CXIG4/16JOdnzjhbtO/dVbbsypbIfTv0t/t4bV4haWF+mjHR3pz05takbNCPdr1qAxxcT3i2NQE\nLY7rUy6NMRdJetBae1358xmSbNUqXXmg+3/W2u81cC8CHQKGx+NRQsJ0ZWRUFJ8lyaO4uOlas+Yp\nZWaGVAa39eulnBxp0KDqlbchQ5xdKAGgPtZa5R7KrVz/lpKXouPFx5UUk1S5Bm5wt8EEuNNQ5inT\nJ7s+qazcnd3+7MrKXb/O/dweXotxqPCQs6lJ9jK9t+09De8xXGMGjNHNA2/WeZ3Oc3t4gOtc3RTF\nGDNW0jXW2p+WP79T0ihr7bQq1yRJWiopX9JuSb+y1mZ5uReBDgEjLS1NiYl5Kii4tdrxkJClCguL\nUWxsQrXwNmyY1Lq1S4NFi9Qc1i20VNZabTu4rdomJiWeksr1b6NjRmtg14FUKnyszFOmVTtXaXHm\nYi3dtFQ92/esrNz17dzX7eE1O/lH8rVi8woty16mT/M/1eiY0RozcIy+F/s9dWvbze3hAQGnqdoW\nyFpb70PSWEnPV3l+p6Q5Na5pJymy/PPrJOXUcS/r7fHggw9abx588EGu5/omu/6//11vw8KWWqcD\nnLVScI2f65v39RsyNti4m+Js5B2RNvKOSHtW7FlBNf6Wdr3H47Gb92+2z61/zt6+9Hbb/ur2QTX+\nlnL9VT+8ym47sC1gxhPs17f+Tmt71xt32aVZS+3RoqOuj4fruT6Qr09KSrIPPvhg5XnbQAY7lUdj\np1zOstZeW/681pRLL1+zQ1KCtfZAjeO2odcDmtLRo9Ibb0gvvSSlpXkUEjJdBw7UnnKZlvYU1ZDT\nQEXJN+rq/ROXEae0N9N4XwOAtVbZ+7Mrp0+m5KUoLCRMSTFJlX3g+nbqSwUuQJR5ypSal6rFmYv1\nRvYb6h3Vu7JyR4+z+lX0CFyWvUzLspepsLSwssn35X0ubxatMgB/M8b4fcplqKTNcjZF2SvpM0m3\nWWs3VbnmLGvt1+Wfj5K02Fob4+VeBDr4XWmp9MEHToj773+lxETprruk731P2rSpYlOUJElS//7J\nmj//Z143RUH9avaqiT0aq3mz5zVJr5rmwForj/Wo1FOqUk+pymxZ5ecb0jbo1udu1YnYE9W+JnJL\npJKnJ+uCkRe4NOqWy2M9ytqX5Wxikpes1LxURYZFVptCGdMxhgAXBEo9pSfD3aY3FN0xunLNXUzH\nGLeHFxCKSoucTU2y39SKzSvUvW33yhAX3yOen3PgDPk90JW/6LWSntbJtgWPGWPukVOpe94Yc5+k\neyWVSDoh6X5r7Vov9yHQwS+slTIynBD36qtSnz5OiPvBD6RuNab1U1U6c42pKFULL54yr0HmVM7V\nPF/fuVO6r/XfGEJMiFqFtFKoCVWrkFaVD88ej77d8600uMYbnSWpoxQZHVmtv02tnjf19MKp71ib\nVm2a9S9qp/Lvusd69MXXX1RW31JyU9SxdcfKFgJJ0UmK7hjtr6GjiZR6SpWSm6LFmYv1ZvabiukY\no/FDxuv7g7/f4sLd4cLDzqYmm5fp3a3vathZwzRm4BjdPOBm1h8CPuZKoPPZixHo0MTy86VFi5wg\nd/SodOedTpAbMMDtkTVPHuvRjoM7tPzj5ZqxdIZKBpRUvyBLCu0cKk8Pj6xstdBSM8iEhoTWea7m\n+frOtTL138tnr3MG9604X1d4qi8gr3tjnQrLCr02N63rWGXj1AYaqhaXFatNqzYNh78GgmLb8NrB\nsnWr1q6GxYYqyGWeMm38emPlJiapeanqGtm1soVAUkySzok6x7Xxo+mVekqVnJtcGe7O63Sexg0e\np3GDxzXb8L77yG5nU5PNy7Rm1xolxSTploG36MbYG9W9bXe3hwc0WwQ6oIaq6+I2bJDGjnVC3GWX\n0czbVzzWo9xDucr8JlOZ+5xH1r4sZe/PVtfIrjrn2Dn6LPMzlQ4srfZ1kVsi9eG0DzXqglFsx36K\nagaQ/kf6a/7D85t0CmuZp0wFJQW1gl5DgfFYScPXlnhKvFYOq4W/0wiKbcPbKiI0ot6wWFdA7r+u\nv378vz/Wyl0rtWrnKvVo16Ny/VtidKJ6tu/ZZO81AltJWUlluFu2eZn6durrhLsh49SnQx+3h3fa\nKtZ+VjT53vLtFt0Qe4PGDBija/pdo3bh7dweItAiEOgA1b8ujtYCp89jPco7lOeEtm+qB7fObTpr\nSPchGtx1sIZ0H6Ih3YZocLfBah/Rnk08mkhzmg5c6il1wmJDVcSawbAR4bLMU1Zv+CvcVaj3096v\n9QcHs8lo7MVjNf4745UYnaiz2p3l0ruDQFZSVqKPcz92wl32MvXv0l/jBo/T9wd/PyjCncd6tDZ/\nbWWIKygpqGzynRidyKYmgAsIdGixTmVdHOrnsR7tPLyzWsUt85tMZe/PVqc2nTSk28nANqS78zEq\nIqree7pRUQIkJyzWFwq/zPhSj771qIoHFFf7usgtkUq9P1UJCQkujRzBpqSsRB/t+EhLspZoWfYy\nxXaJrQx3vTv0dnt4lYpKi/Rx7sdalr1MyzcvV9fIrpUhbsTZI5r1WlkgGBDo0OJUXRd37JizLu7O\nO1kX1xgVwS1rX1a18LZp3yZ1bN2xWqWt4mOH1h1O//WaUUUJzQcVZDSFkrISfbjjQy3JXKJlm5dp\nQJcBlRuquLHe8nDhYb2z9R0ty16md7e9q6Hdh2rMgDG6eeDN6te5n9/HA6BuBDq0CN7WxU2cKF16\nKevivLHWngxuVSpum/ZvUlREVGXFraLaNrjbYHVs3dHtYQN+QwUZTam4rFgfbv9QS7KWaPnm5RrY\ndaDGD3bCXa+oXk32unuO7nE2NcleptW7VisxOrFyUxOmEAOBi0CHZot1cQ2z1mrXkV3K/CazWnjL\n2pel9uHtKytuFdW2wd0Gq1ObTm4PGwgIVJDhD8Vlxfpg+wdOuMtersHdBmv8kPEaO2hsneHuVH42\nKzc1yV6mnG9zdH3/6zVm4Bhd0/catY9o3yTfEwDfItChWWFdnHfWWuUfya8Ma1U3KGkb3rZWxW1I\ntyEENwAIMMVlxXp/2/takrVEKzav0JDuQzR+8HiNHTy2chfVhlpqeKxHn+3+rDLEHSs+VtnkOyk6\niU1NgCBEoEOzwL1cyswAACAASURBVLo4h7VWu4/urhbYKj62adWmWsWtIrx1btPZ7WEDAE5RUWmR\n3t/uhLu3Nr+lod2H6vuDvq/nH31emSMyq63vHJYxTI/9+TGtyFmh5ZuXq3ObzpUhLuHsBDY1AYIc\ngQ5BqyWvi7PWas/RPbXaAWTty1JEq4hqoa1iumSXyC5uDxsA0AQqwt2zK57V2+velgbXuCBLGj5g\nuO689k7dPOBm9e/S35VxAmgaBDoElZa2Ls5aq73H9p7cUbJKeAsPDfdacesa2dXtYQMAXJCWlqbE\nJxNV0L+g2vE2W9po5f0raakBNFMEOgS8YFsXdzobJVhr9dWxr6qFtorg1iqkldeKW7e2AfjNAwBc\nQ0sNoGUi0CFgBeO6uIYWo1tr9fXxr71W3EJMSK2K25BuQwhuAIBGo6UG0PIQ6BBQgnldXF1/Ge21\nupdumHqDsr51dpc0xtQKbUO6D1G3yG4sTAcAnDFaagAti68DXStf3Qgth7d1cffcE/jr4g4XHlbu\noVzlHspV3uE8rV23Vl9EfnEyzElSiPR1168VdTBKD41+SEO6DVH3tt0JbgCAJhMSEsJ6OQCnjUCH\nRqlrXdxTTwXGujhrrQ4WHnTC2qG8asGt4vNST6liOsYopmOMojtEq2ubrgoNCVWZyqrdKzw0XBOG\nTlDCufzPFQAAAIGNQId6eVsXl5zs/3Vx1lrtL9hfLaDlHcpT7uGTnxtjKsNaRXBLjE6s/Lxzm87V\nKm0ej0epK1KV4ak+5TL2aKzi41m7AAAAgMDHGjrU4sa6uIrNR+qqruUdzlNEaIQT2DpGK6ZDzMnP\nywNbx9YdT/l1WYwOAAAAf2JTFDSJpu4X57EefXXsq+rVtUO5yj3sfJ53OE/twttVq65V+7xjtKIi\nos58IN7GxmJ0AAAA+AmBDj5jrbRx48l1cb17n36/uDJPmfYc3eO1spZ7KFe7Du9Sx9YdKytqNYNb\ndMdotQtv1zTfKAAAABAgCHQ4Y7t3S6+8cmr94ko9pco/ku+1upZ7KFe7j+5WlzZd6qyu9enQR5Fh\nkf77JgEAAIAARKDDaU0RbGhdXHFZsXYd3uW1upZ7KFd7j+7VWe3O8lpdi+kYo94deqt1qwDuWQAA\nAAAEAAJdC1dzE4/Yo7GaN3ue1008SkulDz+UFi6U/vN/hUq4cqcuvT5PZw/K1Z6C6sHt62Nfq2f7\nnnVuOnJO1DkKDw3397cLAAAANCsEuhbM4/Eo4ZYEZcRV32Y/LiNOqxav0q6ju7TjYK5WfZmr99fl\n6YtduQrtkqvQznkqNN/qnA7n1LnpSK+oXmoVQhcLAAAAoCkR6FqwtLQ0XfbEZSocUFj9RJYU1jlM\nHc6O0YmvomUOxyihb7RuuDRGFw9yglvP9j0VGhLqzsABAAAASPJ9oKMkE0Q8Ho+KiktrnyhppYiX\nV2rMhAubvF8cAAAAgMBBoAs2aedIQ3KrTbnUhnP0zjshuuwyF8cFAAAAwO8IdEEkJCREYQd+ouK3\nH5R6t5JsiLShvyL236E2bSjJAQAAAC0Na+iCiMfjUZ9xV2h3WAfp9QfLjw5XXNwvlJb2VKPaFwAA\nAABwj6/X0JEAgkixp1jFo7IVtiZSERG5iozM1fDh92vevHsIcwAAAEALxJTLIPLChhfUN3KUDrdd\npIWr0mWMFB//NGEOAAAAaKGYchkkikqL1G9uP/Vd94YmJF6gn/3M7REBAAAAOFVMuWyh5qXPU7/2\n5+vL9y7QXXe5PRoAAAAAgYApl0GguKxYf1z1RyV+vUQJd0tt27o9IgAAAACBgEAXBF7MeFEDOg/W\n/z16oT77zO3RAAAAAAgUBLoAV1JWoj+s/IMmhC1S5KXSeee5PSIAAAAAgYI1dAFu4caF6t+5v/77\nj0s0bZrbowEAAAAQSAh0AaykrESPrnxUN0bNlMcjXXml2yMCAAAAEEgIdAHslS9eUUzHGKUsvFxT\np0rGZ5ubAgAAAGgO6EMXoEo9pRr4zEA9MuoFTbkxSXl57G4JAAAABDv60LUQi75YpF5RvbThjSRN\nmkSYAwAAAFAbFboAVOYp06C/DdJT3/mHJiVeqbVr2d0SAAAAaA6o0LUAr335ms5qd5byV16hSy4h\nzAEAAADwjj50AabMU6ZHVj6ip6+Zo1/eYvTkk26PCAAAAECgokIXYJZkLVGn1p0Utus7KiuTrrrK\n7REBAAAACFRU6AKIx3r0cOrDeuLqJzT310bTptGqAAAAAEDdqNAFkKVZS9UuvJ0GtrpGKSnSnXe6\nPSIAAAAAgYxdLgOEx3o0/B/D9fh3HlfKv65Xaan0xBNujwoAAACAL/l6l0umXAaINze9qdatWiup\n53WaNE9au9btEQEAAAAIdAS6AOCxHs1Ona1HrnhEixYZXXwxrQoAAAAANIxAFwBWbF6hUBOqG/rf\nqLhx0l//6vaIAAAAAAQDNkVxmbVWs1Nma2bSTKWmGpWW0qoAAAAAQONQoXPZf3L+I4/16OYBN2vs\n/0pTp9KqAAAAAEDjNKpCZ4y51hiTbYzJMcb8pp7rLjDGlBhjbvXdEJsva60eSnlIDyQ+oJ07jVJS\npLvucntUAAAAAIJFg4HOGBMi6RlJ10gaIuk2Y8zAOq57TNK7vh5kc/XO1ndUVFakWwbdor//XZo0\nSWrXzu1RAQAAAAgWjZlyOUrSFmttniQZY16TdLOk7BrXTZX0b0kX+HSEzVRFdW5m4kwVngjRvHnS\np5+6PSoAAAAAwaQxUy57SdpV5Xl++bFKxpieksZYa5+VxAqwRnhv23s6VnxMYweP1aJF0sUXS337\nuj0qAAAAAMHEV5uiPCWp6tq6OkPdrFmzKj8fPXq0Ro8e7aMhBI+qa+eMQjRnjvTEE26PCgAAAICv\nJScnKzk5ucnub6y19V9gzEWSZllrry1/PkOStdY+XuWa7RWfSuoq6bikn1prV9S4l23o9VqC97e9\nr2n/N01f3vulVqaG6uc/lzIz2d0SAAAAaO6MMbLW+uw3/8ZU6NZJ6meMiZa0V9IESbdVvcBae16V\nAc6X9FbNMAdHRXXu95f/XqEhoZo7l1YFAAAAAE5Pg4HOWltmjJki6T05a+5esNZuMsbc45y2z9f8\nkiYYZ7Pxce7H2lewTxOGTlBenpScLC1Y4PaoAAAAAASjBqdc+vTFmHKppBeT9KP4H2ni8In6zW+k\nkhLpr391e1QAAAAA/MGNKZfwkZTcFO05uke3n3+7CgpEqwIAAAAAZ6QxbQvgIw+lPKTfXf47tQpp\npUWLpIsuolUBAAAAgNNHoPOTlXkrlXsoV3ecf4eslebMkaZNc3tUAAAAAIIZgc5PZqfO1u8u/53C\nQsOUmiqVlkrf+Y7bowIAAAAQzAh0frB612ptPbBVE4dPlORU56ZMoVUBAAAAgDPDLpd+cO3L1+rW\nQbfqpwk/VV6eNGKElJcntWvn9sgAAAAA+JOvd7mkQtfE1uav1ab9m3R33N2SpGeflSZNIswBAAAA\nOHO0LWhiD6U8pBmXzlB4aLhOnJBeeIFWBQAAAAB8gwpdE1q3e52++OYLTY6fLEm0KgAAAADgUwS6\nJjQ7dbZmXDpDEa0iaFUAAAAAwOcIdE1kw94NSt+brh+N+JEkKTVVKi6mVQEAAAAA3yHQNZHZKbP1\n60t/rdatWkuS5s6Vpk6lVQEAAAAA36FtQRPI+CpD179yvbZN26Y2YW20c6cUH0+rAgAAAKClo21B\nEHg49WH96pJfqU1YG0nS3/8uTZxImAMAAADgW1TofOzzrz/XNS9fo23TtikyLFInTkh9+khr1kj9\n+rk9OgAAAABuokIX4B5OfVi/vPiXigyLlHSyVQFhDgAAAICvEeh86MtvvtTKvJW6d+S9klTZqmDq\nVJcHBgAAAKBZItD50COpj+gXF/9CbcPbSpJWrnRaFXz3uy4PDAAAAECzRKDzkU37NumjHR/p5xf8\nvPJYRXWOVgUAAAAAmgKbovjIHW/coaHdhuq3l/9WkmhVAAAAAKAWNkUJQJv3b9Z7297TfaPuqzz2\n7LO0KgAAAADQtFq5PYDm4NGVj+p/LvwfRUVESZJOnJD+9S+nVQEAAAAANBUqdGdoy7db9M7WdzR1\n1MmtLBctki68kFYFAAAAAJoWge4MPbryUU25YIo6tO4gyWlVMHeuNG2aywMDAAAA0Owx5fIMbDuw\nTf/J+Y+2TttaeWzlSqmoiFYFAAAAAJoeFboz8IeVf9B9F9ynjq07Vh6bM0eaMoVWBQAAAACaHm0L\nTtOOgzs08p8jtWXqFnVu01nSyVYFublS+/bujg8AAABA4KFtQYD446o/6t6R91aGOclpVXDXXYQ5\nAAAAAP5Bhe405B3K04jnRyhnSo66RHaR5LQq6NPHaVXA7pYAAAAAvKFCFwAeW/WYfjrip5VhTpJe\nfZVWBQAAAAD8i10uT9Guw7v0eubrypmaU3nMWmczlMcfd3FgAAAAAFocKnSn6LFVj+nHI36srpFd\nK4+tXCkVFtKqAAAAAIB/UaE7BbuP7NarX76q7CnZ1Y7PnStNnSqFEI8BAAAA+BGbopyCae9MU3ho\nuP5y9V8qj+3cKcXFSXl57G4JAAAAoH6+3hSFCl0j7T26Vy9//rKy7suqdvzZZ6WJEwlzAAAAAPyP\nCl0j3f9/90uSnrz2ycpjJ05I0dHS6tXsbgkAAACgYVToXPDVsa+0YOMCffnzL6sdf/VV6YILCHMA\nAAAA3ME2Ho3wl9V/0Z3D7lTP9j0rj1W0Kpg2zcWBAQAAAGjRqNA14Jvj32he+jx9ce8X1Y6vWkWr\nAgAAAADuokLXgCdWP6Hbht6mXlG9qh2fM0eaMoVWBQAAAADcw6Yo9dhfsF8DnhmgjHsy1LtD78rj\nu3ZJw4fTqgAAAADAqfH1pijUl+rxxOonNH7w+GphTqJVAQAAAIDAQIWuDt8WfKvYZ2K14acbFN0x\nuvJ4RauCTz6R+vd3cYAAAAAAgg4VOj958tMnNXbQ2GphTpJee81pVUCYAwAAAOA2drn04sCJA3p2\n/bNa/5P11Y5XtCp47DGXBgYAAAAAVVCh8+LpT5/WmAFjdG6nc6sdX7VKKiigVQEAAACAwECFroZD\nhYf0t3V/09ofr611bu5caepUWhUAAAAACAxsilLD7JTZ2n5wu14c82K147t2SXFxUm4uu1sCAAAA\nOD2+3hSFCl0VhwsPa+5nc7V68upa5559VrrzTsIcAAAAgMBBoKti7mdzdV2/69S/S/UtLE+ckP71\nL6dVAQAAAAAECgJduaNFR/X02qe16oerap2jVQEAAACAQMT2HuWe+ewZXd33ag3oOqDa8YpWBVOn\nujQwAAAAAKhDowKdMeZaY0y2MSbHGPMbL+dvMsZsNMakG2PWG2Ou9P1Qm86x4mN68tMn9fvLf1/r\n3CefOK0Krr7ahYEBAAAAQD0anHJpjAmR9IykqyTtkbTOGLPcWptd5bIPrLUryq8/X9Kbkvo1wXib\nxN/X/V1XnnulBnUbVOtcRXWOVgUAAAAAAk1j1tCNkrTFWpsnScaY1yTdLKky0FlrC6pc307Sfl8O\nsikdLz6uv675qz6c+GGtc7t2SR98IL3wggsDAwAAAIAGNKbu1EvSrirP88uPVWOMGWOM2STpbUnT\nfDO8pveP9f/Q5dGXa0j3IbXP/UO66y5aFQAAAAAITD7b5dJau0zSMmPMZZJekjTA23WzZs2q/Hz0\n6NEaPXq0r4ZwygpKCvSXNX/Ru3e+W+vciRPSP/9JqwIAAAAApy85OVnJyclNdn9jra3/AmMukjTL\nWntt+fMZkqy19vF6vmabpFHW2m9rHLcNvZ4/PbnmSa3cuVJv/OCNWufmz5eWLJHeftuFgQEAAABo\nlowxstYaX92vMVMu10nqZ4yJNsaES5ogaUWNQfWt8vkISaoZ5gLNiZIT+vPqP2tm0sxa56yV5s6V\npgXNxFEAAAAALVGDUy6ttWXGmCmS3pMTAF+w1m4yxtzjnLbPSxprjJkoqVjScUk/aMpB+8I/N/xT\no3qNUlyPuFrnPvlEOn6cVgUAAAAAAluDUy59+mIBMuWysLRQfef01YoJK5TQM6HW+fHjpcsvp5k4\nAAAAAN9yY8pls/PChhc04uwRXsNcfr7TqmDSJBcGBgAAAACnwGe7XAaLotIiPfbJY3pjfO2NUCTp\n2WelO++UoqL8PDAAAAAAOEUtLtDNz5iv87ufrwt6XVDrXGGh06pg1SoXBgYAAAAAp6hFBbrismL9\ncdUf9fr3X/d6/rXXpJEjpdhYPw8MAAAAAE5Di1pD92LGixrYdaAuOueiWueslebMYSMUAAAAAMGj\nxVToSspK9IeVf9CisYu8nv/kE+nYMemaa/w8MAAAAAA4TS2mQrdw40L179Jfl/S+xOv5uXOd6lxI\ni3lHAAAAAAS7FtGHrqSsRAOeGaAFYxbo8ujLa53Pz5eGDZNyc9ndEgAAAEDToQ/daXjli1cU0zHG\na5iTaFUAAAAAIDg1+wpdqadUA58ZqBduekFJMUm1zhcWStHR0sqV7G4JAAAAoGlRoTtFr37xqnpF\n9fIa5iSnVcGIEYQ5AAAAAMGnWe9yWeYp0yMrH9GzNzzr9XxFq4JHH/XzwAAAAADAB5p1he61L19T\nt8huuiLmCq/nV6+mVQEAAACA4NVsK3QV1bk5186RMd6nqM6ZI02ZQqsCAAAAAMGp2UaZJVlL1Kl1\nJ33nvO94PZ+fL73/vnT33f4dFwAAAAD4SrOs0HmsRw+nPqwnrn6izurcP/5BqwIAAAAAwa1ZBrql\nWUvVLrydrunrfXFcYaH0z386rQoAAAAAIFg1u0DnsR7NTp2tx7/zeJ3VOVoVAAAAAGgOmt0aumXZ\ny9S6VWtd1+86r+crWhVMm+bngQEAAACAjzWrQOexHs1Oma2ZiTPrrM7RqgAAAABAc9GsAt2KzSsU\nYkJ0Y+yNdV4zdy6tCgAAAAA0D8Za678XM8Y21etZa5XwfIJmJs3UmIFjvF6Tny8NGybl5rK7JQAA\nAAD/M8bIWut9OuFpaDZ1qv/k/Ece69HNA26u85p//EO64w7CHAAAAIDmoVnscmmt1ezU2Xog8YE6\n185VtCpITfXz4AAAAACgiTSLCt07W99RYWmhbhl0S53XvP6606pgwAA/DgwAAAAAmlDQBzprrR5K\neUgzE2cqxHj/dipaFUyd6ufBAQAAAEATCvpA996293Ss+JjGDh5b5zVr1khHjkjXXuvHgQEAAABA\nEwvqQFdRnXsg8YE6q3PSyeocrQoAAAAANCdBHXE+2P6BDhYe1LjB4+q8Zvdu6b33pLvv9t+4AAAA\nAMAfgjbQVVTnfn/57xUaElrndbQqAAAAANBcBW3bgo9zP9a+gn2aMHRCndcUFkrPP0+rAgAAAADN\nU9BW6GanzNbvLv9dvdW511+X4uNpVQAAAACgeQrKQJeSm6L8I/m6/fzb67zGWmnuXGnaND8ODAAA\nAAD8KCgD3UMpD+l3l/9OrULqnjG6Zo10+DCtCgAAAAA0X0EX6FbmrVTuoVzdOezOeq+bM0eaMoVW\nBQAAAACaL2Ot9d+LGWPP9PW++9J3N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SyasH9S03bMpjNwSZIkSZrrJjPl8rfAi5P0J3kq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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45c043aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  3.89213624604\n",
      "W1 relative error: 9.56e-08\n",
      "W2 relative error: 7.13e-06\n",
      "W3 relative error: 2.58e-07\n",
      "b1 relative error: 1.79e-09\n",
      "b2 relative error: 1.08e-09\n",
      "b3 relative error: 1.85e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  460.569448454\n",
      "W1 relative error: 3.14e-07\n",
      "W2 relative error: 1.32e-06\n",
      "W3 relative error: 2.44e-07\n",
      "b1 relative error: 4.26e-08\n",
      "b2 relative error: 1.09e-08\n",
      "b3 relative error: 2.56e-08\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print 'Running check with reg = ', reg\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print 'Initial loss: ', loss\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 20.588987\n",
      "(Epoch 0 / 20) train acc: 0.260000; val_acc: 0.114000\n",
      "(Epoch 1 / 20) train acc: 0.440000; val_acc: 0.129000\n",
      "(Epoch 2 / 20) train acc: 0.580000; val_acc: 0.120000\n",
      "(Epoch 3 / 20) train acc: 0.680000; val_acc: 0.138000\n",
      "(Epoch 4 / 20) train acc: 0.860000; val_acc: 0.133000\n",
      "(Epoch 5 / 20) train acc: 0.920000; val_acc: 0.115000\n",
      "(Iteration 11 / 40) loss: 0.427210\n",
      "(Epoch 6 / 20) train acc: 0.960000; val_acc: 0.129000\n",
      "(Epoch 7 / 20) train acc: 1.000000; val_acc: 0.128000\n",
      "(Epoch 8 / 20) train acc: 1.000000; val_acc: 0.128000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.128000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Iteration 21 / 40) loss: 0.001979\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Iteration 31 / 40) loss: 0.000748\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.125000\n"
     ]
    },
    {
     "data": {
      "image/png": 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JymzLAgA4/20kiP1Gkj9Pcu8k11XVNyX54kyrAgDYB6q7N7dBVSX5mumZsZmqqt5sfQAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45c02ef9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 1e-4\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 10.701514\n",
      "(Epoch 0 / 20) train acc: 0.200000; val_acc: 0.096000\n",
      "(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.111000\n",
      "(Epoch 2 / 20) train acc: 0.480000; val_acc: 0.118000\n",
      "(Epoch 3 / 20) train acc: 0.640000; val_acc: 0.111000\n",
      "(Epoch 4 / 20) train acc: 0.840000; val_acc: 0.134000\n",
      "(Epoch 5 / 20) train acc: 0.920000; val_acc: 0.141000\n",
      "(Iteration 11 / 40) loss: 0.573982\n",
      "(Epoch 6 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.152000\n",
      "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.150000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.146000\n",
      "(Iteration 21 / 40) loss: 0.020873\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.147000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.146000\n",
      "(Iteration 31 / 40) loss: 0.033853\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.145000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.146000\n"
     ]
    },
    {
     "data": {
      "image/png": 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L2vgxWna27W+++YPt0KGXtAMHrmsHDlzXLrnkxe3mmz+44TY33XRTO3DgLauO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\nANCJIAYA0IkgBgDQyf8PvFxm/09ndbgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45bc5bf710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3\n",
    "weight_scale = 3e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[we should initialize big networks with big weights]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(next_w, expected_next_w)\n",
    "print 'velocity error: ', rel_error(expected_velocity, config['velocity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: inf\n",
      "(Epoch 0 / 5) train acc: 0.096000; val_acc: 0.110000\n",
      "(Iteration 11 / 200) loss: 5.487596\n",
      "(Iteration 21 / 200) loss: 3.645443\n",
      "(Iteration 31 / 200) loss: 3.338963\n",
      "(Epoch 1 / 5) train acc: 0.196000; val_acc: 0.172000\n",
      "(Iteration 41 / 200) loss: 2.847119\n",
      "(Iteration 51 / 200) loss: 2.604587\n",
      "(Iteration 61 / 200) loss: 2.341769\n",
      "(Iteration 71 / 200) loss: 2.243869\n",
      "(Epoch 2 / 5) train acc: 0.262000; val_acc: 0.202000\n",
      "(Iteration 81 / 200) loss: 2.230681\n",
      "(Iteration 91 / 200) loss: 2.205840\n",
      "(Iteration 101 / 200) loss: 2.058904\n",
      "(Iteration 111 / 200) loss: 2.257936\n",
      "(Epoch 3 / 5) train acc: 0.274000; val_acc: 0.216000\n",
      "(Iteration 121 / 200) loss: 2.056825\n",
      "(Iteration 131 / 200) loss: 2.100731\n",
      "(Iteration 141 / 200) loss: 1.832483\n",
      "(Iteration 151 / 200) loss: 1.852914\n",
      "(Epoch 4 / 5) train acc: 0.347000; val_acc: 0.223000\n",
      "(Iteration 161 / 200) loss: 1.840433\n",
      "(Iteration 171 / 200) loss: 1.876656\n",
      "(Iteration 181 / 200) loss: 1.843678\n",
      "(Iteration 191 / 200) loss: 1.848281\n",
      "(Epoch 5 / 5) train acc: 0.383000; val_acc: 0.232000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: inf\n",
      "(Epoch 0 / 5) train acc: 0.091000; val_acc: 0.114000\n",
      "(Iteration 11 / 200) loss: 3.480034\n",
      "(Iteration 21 / 200) loss: 2.482162\n",
      "(Iteration 31 / 200) loss: 2.401258\n",
      "(Epoch 1 / 5) train acc: 0.171000; val_acc: 0.168000\n",
      "(Iteration 41 / 200) loss: 2.276902\n",
      "(Iteration 51 / 200) loss: 2.283425\n",
      "(Iteration 61 / 200) loss: 2.315206\n",
      "(Iteration 71 / 200) loss: 2.259597\n",
      "(Epoch 2 / 5) train acc: 0.251000; val_acc: 0.203000\n",
      "(Iteration 81 / 200) loss: 2.093768\n",
      "(Iteration 91 / 200) loss: 2.044847\n",
      "(Iteration 101 / 200) loss: 2.244892\n",
      "(Iteration 111 / 200) loss: 2.173730\n",
      "(Epoch 3 / 5) train acc: 0.249000; val_acc: 0.207000\n",
      "(Iteration 121 / 200) loss: 1.987783\n",
      "(Iteration 131 / 200) loss: 2.051976\n",
      "(Iteration 141 / 200) loss: 1.941877\n",
      "(Iteration 151 / 200) loss: 2.084393\n",
      "(Epoch 4 / 5) train acc: 0.316000; val_acc: 0.233000\n",
      "(Iteration 161 / 200) loss: 1.924133\n",
      "(Iteration 171 / 200) loss: 1.931927\n",
      "(Iteration 181 / 200) loss: 1.893479\n",
      "(Iteration 191 / 200) loss: 2.040039\n",
      "(Epoch 5 / 5) train acc: 0.324000; val_acc: 0.240000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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MSZIkSZLhTpIkSZKqgeFOkiRJkqpAzVhXQJKqzcKFC12bSxolCxcuHOsqSNK44YQqkiRJ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45bc522f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print 'running with ', update_rule\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'cache error: ', rel_error(expected_cache, config['cache'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.207207036686\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'v error: ', rel_error(expected_v, config['v'])\n",
    "print 'm error: ', rel_error(expected_m, config['m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: inf\n",
      "(Epoch 0 / 5) train acc: 0.141000; val_acc: 0.114000\n",
      "(Iteration 11 / 200) loss: 6.075204\n",
      "(Iteration 21 / 200) loss: 2.943293\n",
      "(Iteration 31 / 200) loss: 2.529069\n",
      "(Epoch 1 / 5) train acc: 0.174000; val_acc: 0.139000\n",
      "(Iteration 41 / 200) loss: 2.265612\n",
      "(Iteration 51 / 200) loss: 2.421985\n",
      "(Iteration 61 / 200) loss: 2.321561\n",
      "(Iteration 71 / 200) loss: 2.186344\n",
      "(Epoch 2 / 5) train acc: 0.224000; val_acc: 0.193000\n",
      "(Iteration 81 / 200) loss: 2.028834\n",
      "(Iteration 91 / 200) loss: 2.057229\n",
      "(Iteration 101 / 200) loss: 2.092278\n",
      "(Iteration 111 / 200) loss: 2.039083\n",
      "(Epoch 3 / 5) train acc: 0.204000; val_acc: 0.197000\n",
      "(Iteration 121 / 200) loss: 2.040714\n",
      "(Iteration 131 / 200) loss: 2.088442\n",
      "(Iteration 141 / 200) loss: 1.985489\n",
      "(Iteration 151 / 200) loss: 2.138116\n",
      "(Epoch 4 / 5) train acc: 0.230000; val_acc: 0.220000\n",
      "(Iteration 161 / 200) loss: 1.976416\n",
      "(Iteration 171 / 200) loss: 1.917521\n",
      "(Iteration 181 / 200) loss: 2.094736\n",
      "(Iteration 191 / 200) loss: 1.986792\n",
      "(Epoch 5 / 5) train acc: 0.274000; val_acc: 0.253000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: inf\n",
      "(Epoch 0 / 5) train acc: 0.106000; val_acc: 0.128000\n",
      "(Iteration 11 / 200) loss: 7.366343\n",
      "(Iteration 21 / 200) loss: 5.220843\n",
      "(Iteration 31 / 200) loss: 4.173086\n",
      "(Epoch 1 / 5) train acc: 0.270000; val_acc: 0.188000\n",
      "(Iteration 41 / 200) loss: 3.416109\n",
      "(Iteration 51 / 200) loss: 3.289071\n",
      "(Iteration 61 / 200) loss: 3.340670\n",
      "(Iteration 71 / 200) loss: 2.710267\n",
      "(Epoch 2 / 5) train acc: 0.332000; val_acc: 0.202000\n",
      "(Iteration 81 / 200) loss: 2.638055\n",
      "(Iteration 91 / 200) loss: 2.051805\n",
      "(Iteration 101 / 200) loss: 2.225369\n",
      "(Iteration 111 / 200) loss: 2.059026\n",
      "(Epoch 3 / 5) train acc: 0.403000; val_acc: 0.218000\n",
      "(Iteration 121 / 200) loss: 2.131304\n",
      "(Iteration 131 / 200) loss: 2.270122\n",
      "(Iteration 141 / 200) loss: 1.531396\n",
      "(Iteration 151 / 200) loss: 1.710008\n",
      "(Epoch 4 / 5) train acc: 0.475000; val_acc: 0.227000\n",
      "(Iteration 161 / 200) loss: 1.672730\n",
      "(Iteration 171 / 200) loss: 1.911606\n",
      "(Iteration 181 / 200) loss: 1.545359\n",
      "(Iteration 191 / 200) loss: 1.627064\n",
      "(Epoch 5 / 5) train acc: 0.507000; val_acc: 0.232000\n",
      "\n"
     ]
    },
    {
     "data": {
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HgLy8vGb3//nPfwLx8HbGGWcwa9Ys8vPz+cY3vpE2f278+PHJrydOnMgnn3zCzp07Wzzv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2SmIdwBADCAtrbk+8UVFiYubrJypfv7BgBAk64dMlMQ7gAAk15XV2S/uOi5ce3tiSGOeXEAJqVU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fn0oB/v4NJCIiIlKx+fIr9u7AFmttMoAxZj5wM1Ac7qy16UC6MeaGI59ojKkD9LbWDvReVwhk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\nAVvgiPa4FCLyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45bc8bc390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print 'running with ', update_rule\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(X_test), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(X_val), axis=1)\n",
    "print 'Validation set accuracy: ', (y_val_pred == y_val).mean()\n",
    "print 'Test set accuracy: ', (y_test_pred == y_test).mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
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